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Fabrication & Testing of Plasmochromic Devices Based on Blended Films of Organic Semiconductors:
Project Title: Fabrication & Testing of Plasmochromic Devices Based on Blended Films of Organic Semiconductors
| Term: | Winter 2026 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: Electrochromic devices (ECDs) change their color or more generally their complex refractive index in response to an applied bias. Plasmonics is the science and engineering of the optically stimulated coherent and collective oscillations of conduction band electrons in coinage metals such as Au, Ag and Cu. Plasmochromic devices (PCDs) exploit the interaction of plasmonics and electrochromism to achieve "active control" of the surface plasmon resonance phenomenon. The Shankar Lab is collaborating with Canadian industry to fabricate and test optoelectronic devices based on new organic semiconductors, some of which will be tested in PCDs. The DRA intern will work closely with graduate students in the Shankar Research group to build and test ECDs and PCDs based on blended films of organic semiconductors.
4d Printed light-weight materials production and characterization: :
Project Title: 4d Printed light-weight materials production and characterization:
| Term: | Fall 2025 |
Professor: | Ayranci, Cagri |
Email: | cayranci@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: Shape memory polymers are smart materials that can change shape in response to a stimulus.
4D printing is production of these materials with additive manufacturing and controlling the activation via polymer properties. With this, a temporary shape can be formed, and later the permanent shape can be recalled.
One missing element in this link is reducing the weight of these novel materials. This project deals with the production and characterization of lightweight multifunctional materials, and production of design curves for their broader usage.
4D printed structural health monitoring:
Project Title: 4D printed structural health monitoring
| Term: | Fall 2025 |
Professor: | Ayranci, Cagri |
Email: | cayranci@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: Structural Health Monitoring (SHM) is a crucial aspect of monitoring structures during their useful lifes. This project aims to produce parts and components made out of Shape Memory Polymers using Additive Manufacturing (4D printing) with integrated SHM Capabilities, and test and calibrate these sensors.
Additive manufacturing in steel:
Project Title: Additive manufacturing in steel
| Term: | Fall 2025/Winter 2026 |
Professor: | Mendez, Patricio |
Email: | ccwj@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: Additive manufacturing, or creating an object layer by layer, is an emerging technology in various industries such as oil and gas, aerospace, automotive, and many others. This project focuses on robotic welding technology and the 3D printing of metal components. During the term, you will become proficient in operating a welding robotic machine designed specifically for this purpose. You will have the opportunity to select and modify various printing parameters to observe and analyze their effects on the final printed components for application in these industries. These skills are crucial for advancing material properties, optimizing processes, and improving component design and performance in various industries.
Additive manufacturing of civil components:
Project Title: Additive manufacturing of civil components
| Term: | Fall 2025/Winter 2026 |
Professor: | Mendez, Patricio |
Email: | ccwj@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: Additive manufacturing, or creating an object layer by layer, is an emerging technology in various structural industries and materials. In civil engineering, AM is revolutionizing the way structural components are designed and constructed. This technology enables the production of complex geometries and customized parts that traditional methods struggle to achieve. For example, AM is used in the construction of bridges, custom building components, and infrastructure repairs, offering enhanced performance and unique design possibilities. It also facilitates rapid prototyping and the creation of lightweight yet strong metal frameworks, thereby reducing material waste and improving overall efficiency in structural projects. As a civil engineering student, exploring AM's applications in metal 3D printing provides a valuable opportunity to innovate and advance structural design and construction techniques.
Advanced Machine Learning-Based Propagation Models for B5G/6G Wireless Communications:
Project Title: Advanced Machine Learning-Based Propagation Models for B5G/6G Wireless Communications
| Term: | Fall 2025/Winter 2026 |
Professor: | Zhang, Xingqi (DICE 11-381) |
Email: | xingqi.zhang@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: The emergence of new wireless communication technologies and systems creates an urgent need for intelligent planning of a plethora of existing and emerging B5G/6G wireless services. To this end, radio wave propagation models are a necessary prerequisite, as they can predict the signal levels created by a system of transmitters in a given environment. Such models can be used to optimize the position of network access points, assess interference from and towards neighbouring systems and perform network-level performance evaluation studies. Radio wave propagation models can be derived by physics-based methods (e.g., ray-tracing, full-wave electromagnetic modeling techniques). However, the development of physics-based models demands a high level of relevant expertise and computational resources that can, in practice, be prohibitive for real-time B5G/6G wireless applications. This project aims to explore a data-driven approach that can lead to computationally efficient, high-fidelity radio wave propagation models, without performing a channel simulation, by leveraging advances in deep learning (e.g., physics-informed neural network, PINN). The goal is to build a learning-centred methodology that can recognize the signal propagation and fading characteristics of a channel over a frequency bandwidth, by processing the channel geometry.
Advanced Manufacturing of Polymers for Defence and Aerospace Applications:
Project Title: Advanced Manufacturing of Polymers for Defence and Aerospace Applications
| Term: | Fall 2025/Winter 2026 |
Professor: | Hogan, James (DICE 10-227) |
Email: | jdhogan@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: I am seeking motivated students to explore how advanced manufacturing can be applied to reproduce and improve polymer-based components used in defence applications. This project will advance understanding of how digital design, scanning, and additive manufacturing workflows can be combined to fabricate high-performance parts for demanding environments.
The project steps include 3D scanning of existing polymer defence components to capture geometry and structural details. The scanned data will then be reconstructed into digital models, which can be optimized for manufacturing. Next, students will fabricate parts using polymer-based additive manufacturing techniques, such as extrusion-based 3D printing. Printed components will be characterized through mechanical testing and compared to original parts in terms of accuracy, strength, and durability.
Proficiency in Matlab or Python is encouraged. Experience with 3D scanning or 3D printing software is an asset.
Agentic AI for Structural Design:
Project Title: Agentic AI for Structural Design
| Term: | Fall 2025/Winter 2026 |
Professor: | Mei, Qipei |
Email: | qipei@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: This project explores the integration of agentic AI systems into the structural design process, enabling autonomous reasoning, decision-making, and collaboration with human engineers. By combining advanced generative AI with structural engineering principles, the system can analyze design constraints, generate optimized structural solutions, and iteratively adapt to project requirements such as safety, cost, material efficiency, and sustainability. The goal is to accelerate design workflows, reduce human error, and unlock innovative approaches to resilient and low-carbon structures.
AI-aided navigation of magnetotactic microswimmers:
Project Title: AI-aided navigation of magnetotactic microswimmers
| Term: | Fall 2025/Winter 2026 |
Professor: | Peng, Zhiwei (ICE 12-324) |
Email: | zhiwei.peng@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: This undergraduate project explores the use of artificial intelligence (AI) to aid in the navigation and control of magnetotactic swimmers—microscale organisms or synthetic particles that align and move along magnetic field lines. The goal is to develop and test AI-based algorithms capable of dynamically adjusting magnetic field parameters to guide swimmers through complex environments. By combining concepts from fluid mechanics, control theory, and machine learning, the project provides students with hands-on experience in both physical modeling and machine learning, with potential applications in targeted drug delivery, environmental remediation, and microscale robotics.
AI-Enabled Solutions for Advancing Winter Transportation in Edmonton:
Project Title: AI-Enabled Solutions for Advancing Winter Transportation in Edmonton
| Term: | Winter 2026 |
Professor: | Kwon, Tae J. (DICE 6-281) |
Email: | tjkwon@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: Canadian cold-weather cities face growing challenges in winter transportation management due to climate change, expanding infrastructure, and budget pressures. This project, in partnership with the City of Edmonton’s Snow and Ice Control program, will develop an AI-powered Winter Surface Condition Monitoring tool to detect and classify hazardous road conditions in real time. The student will assist by preparing and analyzing datasets such as RWIS records, roadway imagery, and weather data, applying AI and machine learning methods, and testing a prototype application. This project offers a unique opportunity to gain hands-on experience with AI in transportation and contribute to a research initiative with direct operational impact. While prior experience with AI modeling or web app development is an asset, motivated senior undergraduates with strong analytical skills are encouraged to apply.
AI-Powered Physics: Simulating Fluids with Neural Networks:
Project Title: AI-Powered Physics: Simulating Fluids with Neural Networks
| Term: | Winter 2026 |
Professor: | Zhang, Xuehua (12-380) |
Email: | xuehua1@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: Computational Fluid Dynamics (CFD) is essential for engineering, but high-accuracy simulations are computationally expensive and slow. Our group is exploring how Artificial Intelligence can speed this up. We are looking for an undergraduate student to work on a "proof-of-concept" project using Machine Learning (ML) to predict flow velocity in simple geometries.
This is a fantastic opportunity for an engineering or computer science student to gain hands-on experience at the intersection of Physics and AI. Instead of building a complex system from scratch, you will focus on a specific test case (e.g., flow in a pipe).
What You Will Learn
• Applied AI: Practical experience building and training neural networks using Python.
• Fluid Mechanics: Understanding how CFD data is structured and analyzed.
• Data Science: Skills in data pre-processing, normalization, and visualization.
• Research Skills: How to document experiments and present scientific results.
Alloy design for new High Entropy Alloys:
Project Title: Alloy design for new High Entropy Alloys
| Term: | Fall 2025/Winter 2026 |
Professor: | Henein, Hani (12-219 DICE Building) |
Email: | hani.henein@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: High Entropy alloys (HEAs) and near stoichiometric HEAs (all referred as HEAs) are a new generation of materials that show promise to provide a unique combination of properties. Given the enormous possibilities in tuning the structures and compositions of HEAs, this project aims to test our model for predicting new HEA alloys. Thus, a novel high throughout method has been developed to evaluate candidate HEAs.
Analyzing the optical properties of electropolymerized organic semiconductors:
Project Title: Analyzing the optical properties of electropolymerized organic semiconductors
| Term: | Winter 2026 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: The Shankar Lab is collaborating with Canadian industry to fabricate and test optoelectronic devices based on new organic semiconductors which have applications in displays (e.g. OLEDs), solar energy conversion (OPV) and flexible/wearable electronics. This project involves forming thin coatings of electrochromic organic semiconductors through electropolymerization of monomer mixtures. A key metric to measure for any new organic semiconductor is the permittivity function. The relative permittivity (also known as dielectric constant) of a material is a complex function of frequency with both real and imaginary components at each frequency. If the relative permittivities of thin organic semiconductor films are quantified over the entire ultraviolet, visible and near-infrared spectral range, the optical and electronic properties of the organic semiconductor are determined to a large extent. The hands-on component of this project involves mastering an instrument known as an ellipsometer.
Analyzing the optical properties of spin-coated organic semiconductor thin films and blends:
Project Title: Analyzing the optical properties of spin-coated organic semiconductor thin films and blends
| Term: | Winter 2026 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: The Shankar Lab is collaborating with Canadian industry to fabricate and test optoelectronic devices based on new organic semiconductors (OSCs) which have applications in displays (e.g. OLEDs), solar energy conversion (OPV) and flexible/wearable electronics. The solution processability of OSC films are a key attraction. A key metric to measure for any new organic semiconductor is the permittivity function. The relative permittivity of a material is a complex function of frequency with both real and imaginary components at each frequency. If the relative permittivities of thin OSC films are quantified over the entire UV, visible and NIR spectral range, the optical and electronic properties of the OSC are determined to a large extent. The hands-on component of this project involves mastering an instrument known as an ellipsometer which uses polarized light to collect permittivity data. The data analysis component of this project is math-intensive.
Backwater Effects and Ice-Jam Plugs - An overlooked mechanism at Confluences:
Project Title: Backwater Effects and Ice-Jam Plugs - An overlooked mechanism at Confluences
| Term: | Fall 2025 |
Professor: | Nafziger, Jennifer (DICE 7-209) |
Email: | jnafzige@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: Ice jams are an important cause of flooding in cold regions like Canada. River confluences are common locations of ice jam formation. Several mechanisms of ice jam formation at confluences have been proposed in the literature. However, mechanism has been observed and noted in previous case study reports, but has not been fully reported in the literature. This is the "Backwater/Ice Jam Plug" mechanism. It is important for several locations in northern Alberta, including at Fort McMurray. The DRA student will review historical remote sensing data and reporting and find instances of this mechanism. They will also review geofabric data of the rivers and for information such as river slope and channel width, and identify patterns or salient information regarding these instances. This work will contribute to a journal article on this subject. This is a good project for a student interested in Water Resources Engineering, River Engineering, Remote Sensing, fluvial geomorphology, etc.
Calibration-free laser-induced breakdown for heterogeneous materials analysis:
Project Title: Calibration-free laser-induced breakdown for heterogeneous materials analysis
| Term: | Fall 2025/Winter 2026 |
Professor: | Hussein, Amina |
Email: | aehussei@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: Laser-induced breakdown spectroscopy (LIBS) is an optical spectroscopy technique used for elemental analysis of diverse materials. Recent advances in machine learning and artificial intelligence models enable the use of LIBS in heterogeneous materials analysis, such as soil, wheat, and animal feed, by overcoming physical and chemical matrix effects. However, machine learning-based calibration of LIBS requires a large calibration dataset. We aim to address this critical challenge by developing calibration-free LIBS (CF-LIBS) for diverse applications. CF-LIBS is based on the calculation of plasma electron temperature and number density, and it has the advantage of not requiring any reference materials from a specific sample to generate calibration curves. The success of CF-LIBS is dependent on developing a precise description and understanding of the physical states of laser-induced plasmas using theoretical modelling. In this research project, we will first experiment with metal and alloy samples such as aluminium (Al) and brass to develop a CF-LIBS model, as metals and alloys have well-known physical and atomic properties. Following the successful creation of the CF-LIBS model for metal/alloys, we will create CF-LIBS models for more complex heterogeneous material analysis, such as animal feed and soil.
Carbon Fiber Robotic 3D printing of large scale prints:
Project Title: Carbon Fiber Robotic 3D printing of large scale prints
| Term: | Fall 2025/Winter 2026 |
Professor: | Qureshi, Ahmed Jawad (10-361 DICE) |
Email: | ajquresh@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: This project lies at the intersection of Mechanical Engineering and Mechatronics Engineering. The student will work with the research time to design and optimize the carbon fiber flow path in a large-scale robotic 3D printer installed in MECE 2-9 lab.
The main activities will be:
- Designing components in Solidworks
- Prototyping and Manufacturing with 3D printing and conventional manufacturing techniques
- Running design of experiments for characterization and optimization
The required skills are:
- Proficiency with Solidworks modeling. CSWA preferred
- Experience in programming
Interested students can contact Minahil Tauqir at mtauqir@ualberta.ca to get more information about the project.
Carrier dynamics in core-shell chalcogenide nanorods:
Project Title: Carrier dynamics in core-shell chalcogenide nanorods
| Term: | Winter 2026 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: The Shankar Lab has strong expertise in the synthesis & application of nanoparticles and nanorods of inorganic chalcogenides such as CdS, CdSe, PbS and CdTe. The core-shell structure (e.g. CdSe@CdS, CdTe@Au)) passivates defect states and allows excellent control over the optoelectronic properties. The objectives here are two-fold. First, to use sustainable synthetic protocols to achieve core-shell nanorods with a narrow size dispersion. Second, to perform time-resolved spectroscopic and electrical characterization of the nanorods to obtain insight into carrier dynamics. A portion of this research may be conducted in the Faculty of Science.
Curbside Management: A Data-Driven Approach to Equitable and Efficient Parking Utilization:
Project Title: Curbside Management: A Data-Driven Approach to Equitable and Efficient Parking Utilization
| Term: | Fall 2025 |
Professor: | El-Basyouny, Karim |
Email: | basyouny@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: Curbside spaces are essential yet limited resources in urban infrastructure. This project aims to assess the usage of on-street parking in residential areas, with the potential to improve urban mobility. The presence of non-parking elements, such as fire hydrants, personal driveways, no-parking signage, and crosswalks, reduces the effective parking space available. This research will focus on utilizing video footage data to identify and track cars parked on the curbside alongside the various curbside elements impacting the effective available parking length. The student will contribute to developing an advanced deep learning (DL) model for automated detection and tracking of these assets. The role will provide valuable experience in computer vision, DL frameworks, and models’ optimization. Prior experience with machine learning, object detection, and model tuning is beneficial; however, highly motivated undergraduate students interested in these fields are encouraged to apply.
Deep learning to interpret images and videos:
Project Title: Deep learning to interpret images and videos
| Term: | Fall 2025/Winter 2026 |
Professor: | Cheng, Li (ICE, 11-365) |
Email: | lcheng5@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: This project focuses on developing deep learning techniques to interpret images/videos. You are expected to work with a graduate student/researcher, get familiar with state-of-the-art deep learning techniques, and gain hands-on research experience on benchmark and home-grown datasets. It is also a good opportunity to have a taste of & participate into the computer vision and machine learning related research projects carried out in our lab.
Design and Evaluation of a HVAC Platform for Indoor Air Quality Experiments:
Project Title: Design and Evaluation of a HVAC Platform for Indoor Air Quality Experiments
| Term: | Fall 2025 |
Professor: | Zhong, Lexuan (ICE 10-215) |
Email: | lexuan.zhong@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: This project focuses on developing an interactive IAQ demonstration platform to support undergraduate research and learning in HVAC engineering. The student will assist in constructing and testing a portable air duct system equipped with a fan, coils, duct-mount humidifier, and air quality sensors (CO₂, VOCs, temperature, humidity, etc.). The system will be used to simulate real-world indoor environments and evaluate the impact of various HVAC configurations on IAQ performance.
The student will be involved in data collection and analysis using environmental meters, as well as helping design hands-on demonstrations for integration into undergraduate HVAC courses (MEC E 463/466). They will gain experience in HVAC operations, sensor calibration, airflow measurement, and IAQ assessment methods.
Design and Optimization of High-Performance DC-DC Converters for Modern Applications:
Project Title: Design and Optimization of High-Performance DC-DC Converters for Modern Applications
| Term: | Fall 2025/Winter 2026 |
Professor: | Zhang, Zhenyu (11-369, Donadeo Innovation Centre Of Engineering) |
Email: | zhenyu15@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: This project focuses on developing an improved design for DC-DC converters, a key component in power electronics systems. These converters are widely used in electronic devices, industrial equipment, and other technologies that require efficient power regulation. The student will explore innovative circuit structures that aim to enhance stability, reduce output fluctuations, and improve response time under dynamic conditions.
Design and Simulation Development of a Rehabilitation Robotic System:
Project Title: Design and Simulation Development of a Rehabilitation Robotic System
| Term: | Fall 2025/Winter 2026 |
Professor: | Nazarahari, Milad |
Email: | nazaraha@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: Rehabilitation robotics is transforming healthcare by supporting motor recovery in patients with neuromuscular impairments. Our lab has developed a robotic prototype, but to move from a lab device toward a presentation-ready system, we need contributions in design and simulation.
The project has two tracks:
(1) Hardware design – create professional CAD models (and potentially prototyping) for enclosures that house the robot and electronics, ensuring only the end-effector is visible, while emphasizing ergonomics, safety, and presentation quality.
(2) Modelling and simulation – develop kinematic/dynamic models and run finite element analysis (FEM) to study performance, safety, and structural integrity.
Students will gain hands-on experience in CAD, robotics, simulation, and FEM software, contributing to a real-world medical robotics system. This project offers the chance to design, model, and validate cutting-edge technology that bridges engineering with healthcare.
Development of a Low Cost, High Precision Ozone (O₃) Detection System:
Project Title: Development of a Low Cost, High Precision Ozone (O₃) Detection System
| Term: | Fall 2025 |
Professor: | Zhang, Zhenyu (11-369, Donadeo Innovation Centre Of Engineering) |
Email: | zhenyu15@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: This project focuses on the development of a cost effective and high precision ozone (O₃) detection device designed for standalone operation with support for both wired and wireless communication. By utilizing an array of gas sensors and applying data fusion techniques, the system aims to significantly improve measurement accuracy and repeatability, overcoming the inherent limitations of individual low cost sensors. The outcome of this research will contribute to the advancement of accessible air quality monitoring technologies.
Development of a microstructure digital library for additive manufacturing:
Project Title: Development of a microstructure digital library for additive manufacturing
| Term: | Fall 2025/Winter 2026 |
Professor: | Henein, Hani (12-219 DICE Building) |
Email: | hani.henein@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: As more industries move to capitalize on the technological benefits of additive manufacturing, researchers are exploring ways to design new alloys with properties that cannot be achieved through traditional manufacturing methods. One approach is to tailor the solidification microstructures of lightweight components using dense materials such as eutectics. Eutectics are natural composite materials that are composed of a ductile phase and a brittle phase. This study examines the microstructures and mechanical properties of near eutectics under different thermal histories found in various additive manufacturing techniques. Rapidly solidified powders of various sizes are generated by atomization. Microstructural analysis will reveal the different eutectic morphologies and spacing depending on the cooling rate. The aim of this study is to develop Solidification Microstructure Maps of eutectics as well as Continuous Heating Transformation Diagrams. These will serve as a microstructure digital library of additive manufacturing processes.
Drop Impact on Heated Surfaces:
Project Title: Drop Impact on Heated Surfaces
| Term: | Fall 2025/Winter 2026 |
Professor: | Tsai, Amy |
Email: | peichun.amy.tsai@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: In this DRA project, the student will utilize a high-speed camera to record droplets impacting onto heated surfaces with complex structures to explore the effect of surface thermal properties on the impact events. The skills gained include basic instrumentation, image analysis, and hands-on experimental experience.
Effect of synthesis method on the optoelectronic properties of carbon nitrides:
Project Title: Effect of synthesis method on the optoelectronic properties of carbon nitrides
| Term: | Fall 2025 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: Carbon nitride (g-C3N4) is a 2-dimensional sheet-like material which is a cousin of graphene. Unlike graphene which is quasi-metallic, C3N4 is semiconducting which enables it to be used as the "active layer" in optoelectronic devices such as diodes, solar cells, photocatalysts and transistors. One limitation of conventional C3N4 is its relatively wide electronic bandgap (Eg=2.7 eV) which renders it primarily sensitive to blue and ultraviolet photons. The Shankar Lab is working to synthesize narrow bandgap carbon nitrides which absorb a broader swathe of visible photons. This project will explore 2 different methods to synthesize narrow bandgap carbon nitrides: (1) Electropolymerization and (2) Thermal polycondensation. Subsequently, the optoelectronic properties of the synthesized carbon nitrides will be measured using UV-vis spectroscopy, photoluminescence spectroscopy, current-voltage measurements, etc. This project is well-suited to students passionate about materials science.
Electrical testing of hybrid heterojunction architectures:
Project Title: Electrical testing of hybrid heterojunction architectures
| Term: | Fall 2025/Winter 2026 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: A p-n junction, n-n junction or p-p junction between dissimilar semiconducting materials is called a heterojunction (HJ). Organic semiconductors exhibit profound differences from inorganic semiconductors in their underlying physics. "Hybrid" devices are those that involve the simultaneous use of organic and inorganic semiconductor components in the "active layer". Symmetric and linear current-voltage (J-V) curves imply ohmic conduction while asymmetric, logarithmic J-V curves imply diode-like behavior. Sometimes, the hybrid device exhibits electrical behavior that cannot be easily classified as either ohmic or diode-like. This project involves fabricating hybrid device architectures and subsequently testing the electrical properties of the resulting heterojunctions.
Ellipsometric studies of ultrathin organic films:
Project Title: Ellipsometric studies of ultrathin organic films
| Term: | Fall 2025/Winter 2026 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: Ultrathin organic films in the form of self-assembled monolayers and few molecule-thick coating are increasingly important in a variety of high-tech applications such as solar cells, electrochromic devices, biomarker recognition, supercapacitors, and water- and oil-repellant surfaces. Because these films have a thickness smaller than 10 nm, they are difficult to examine via traditional spectroscopic techniques such as spectrophotometry, fluorimetry, Raman and infrared spectroscopy due to a poor signal-to-noise ratio (SNR). Ellipsometry, which examines the response of such thin films to polarized light, enables measurement of the optical and electronic properties of ultrathin organic films while obtaining an adequate SNR. The data analysis component of this project is math-intensive.
Experimental Assesment of Emulsion Liquid Membranes (ELM) & Green Emulsion Liquid Membranes (GELM):
Project Title: Experimental Assesment of Emulsion Liquid Membranes (ELM) & Green Emulsion Liquid Membranes (GELM)
| Term: | Winter 2026 |
Professor: | Dubljevic, Stevan |
Email: | dubljevi@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: We are seeking a motivated undergraduate student to join our ongoing research on sustainable separation processes. The project focuses on Emulsion Liquid Membrane (ELM) and Green Emulsion Liquid Membrane (GELM) systems designed to selectively recover rare earth elements from industrial waste and residuals.
Key responsibilities include conducting experiments in emulsion preparation and characterizing separation conditions (such as pH, surfactant concentration, and emulsion stability). The student will also operate and troubleshoot extraction columns—specifically pulsed-packed columns—manage experimental design, and perform data analysis. Finally, the role involves data collection and data-driven modeling to enhance overall extraction efficiency and selectivity.
Experimental Leak Detection and Localization in Laboratory Pipeline Manifold :
Project Title: Experimental Leak Detection and Localization in Laboratory Pipeline Manifold
| Term: | Winter 2026 |
Professor: | Dubljevic, Stevan |
Email: | dubljevi@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: Pipelines are essential components of industrial infrastructure, enabling the transpor of water, energy, and other critical resources. Ensuring their long-term operational integrity is vital for sustainability, minimizing resource losses and preventing environmental harm. We are seeking a motivated student to perform experimental realization of leak detection in laboratory pipeline manifold.
Exploring Large Language Models for Software Log Analysis:
Project Title: Exploring Large Language Models for Software Log Analysis
| Term: | Fall 2025 |
Professor: | Li, Xingyu |
Email: | xingyu@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: This project aims to investigate how large language models can be applied to improve software log analysis. The work will involve developing methods to create rich feature representations from log data and exploring ways to enhance them with lightweight trainable components.
Fabrication & Testing of Electrochromic & Plasmochromic Devices Based on Electropolymerized Copolymers:
Project Title: Fabrication & Testing of Electrochromic & Plasmochromic Devices Based on Electropolymerized Copolymers
| Term: | Fall 2025 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: Electrochromic devices change their color or more generally their complex refractive index in response to an applied bias. Plasmonics is the science and engineering of the optically stimulated coherent and collective oscillations of conduction band electrons in coinage metals such as Au, Ag and Cu. Plasmochromism exploits the interaction of plasmonics and electrochromism to achieve "active control" of the surface plasmon resonance phenomenon. This project involves forming thin coatings of electrochromic organic semiconductors through electropolymerization of monomer mixtures. The goal of the project is to contrast the structure, optoelectronic properties and electrochromic & plasmochromic behavior of copolymers vs regular polymers. The DRA intern will work closely with graduate students in the Shankar Research group.
Fabrication & Testing of Hybrid Organic-Inorganic Plasmochromic Devices Based on 1D Nanostructures:
Project Title: Fabrication & Testing of Hybrid Organic-Inorganic Plasmochromic Devices Based on 1D Nanostructures
| Term: | Winter 2026 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: Electrochromic devices change their color or more generally their complex refractive index in response to an applied bias. Plasmonics is the science and engineering of the optically stimulated coherent and collective oscillations of conduction band electrons in coinage metals such as Au, Ag and Cu. Plasmochromism exploits the interaction of plasmonics and electrochromism to achieve "active control" of the surface plasmon resonance phenomenon. This project involves decorating inorganic semiconductor nanotubes & nanowires with colloidal gold and silver nanoparticles and subsequently coating them with thin layers of electrochromic organic semiconductors. The goal of the project is to actively control the plasmon resonance frequency, resonance amplitude and response time using plasmochromism. The DRA intern will work closely with graduate students in the Shankar Research group.
Fabrication & Testing of Nanostructured Hybrid Plasmochromic Devices:
Project Title: Fabrication & Testing of Nanostructured Hybrid Plasmochromic Devices
| Term: | Fall 2025 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: Electrochromic devices change their color or more generally their complex refractive index in response to an applied bias. Plasmonics is the science and engineering of the optically stimulated coherent and collective oscillations of conduction band electrons in coinage metals such as Au, Ag and Cu. Plasmochromism exploits the interaction of plasmonics and electrochromism to achieve "active control" of the surface plasmon resonance phenomenon. This project involves decorating inorganic semiconductor nanotubes & nanowires with colloidal gold and silver nanoparticles and subsequently coating them with thin layers of electrochromic organic semiconductors. The goal of the project is to actively control the plasmon resonance frequency, resonance amplitude and response time using plasmochromism. The DRA intern will work closely with graduate students in the Shankar Research group.
Generative AI Models for Advanced Natural Language Processing in Smart City Applications:
Project Title: Generative AI Models for Advanced Natural Language Processing in Smart City Applications
| Term: | Fall 2025/Winter 2026 |
Professor: | Gul, Mustafa |
Email: | mustafa.gul@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: This project focuses on developing advanced generative AI models to enhance natural language processing (NLP) for smart city applications. The primary objective is to build models capable of efficiently extracting and synthesizing critical information from complex, unstructured text data generated in urban environments. This may include data from social media, public service reports, sensor networks, and urban planning documents. By leveraging state-of-the-art machine learning and deep learning techniques, the project seeks to improve the accuracy and responsiveness of smart city systems, enabling more informed decision-making in areas such as infrastructure management, public safety, and environmental monitoring. Ultimately, the project aims to advance intelligent, data-driven solutions that promote the sustainability and livability of urban areas.
GeoResource Agent: Generative AI for Smarter Civil Engineering, Energy and Mining Applications:
Project Title: GeoResource Agent: Generative AI for Smarter Civil Engineering, Energy and Mining Applications
| Term: | Fall 2025/Winter 2026 |
Professor: | Zhang, Bo (DICE 6-239) |
Email: | bzhang7@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: GeoResource Agent is a cutting-edge AI assistant that combines LangChain with powerful APIs and developed tools to tackle real-world challenges in earth and energy sciences.
As part of our team, students will be able to:
1. Experiment with LangChain – a framework that lets large language models connect with tools, memory, and workflows.
2. Work with real-world data – from geological surveys, satellite imagery, and simulation APIs.
3. Build smart applications – that can answer natural language questions, automate analysis, and support decision-making in georesources (like hydrogen, CO₂ storage, oil sands, or geothermal).
Collaborate across disciplines – blending Civil Engineering, petroleum engineering, mining engineering, computer engineering/science and data science.
This project is perfect for students who want hands-on experience and build skills in civil engineering, energy/mining applications, AI agents and data science.
GIS-Based Framework for City-wide Traffic Volume Estimations:
Project Title: GIS-Based Framework for City-wide Traffic Volume Estimations
| Term: | Winter 2026 |
Professor: | Kwon, Tae J. (DICE 6-281) |
Email: | tjkwon@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: Traffic volume data is critical for urban planning and congestion management. This project, developed with the City of Edmonton, will integrate traffic volume estimation, strategic counter placement, and a GIS-based analytics platform to improve network efficiency and sustainability. The student will assist graduate researchers in preparing and analyzing traffic datasets, applying AI-driven spatial analytics, and contributing to the development of a GIS visualization tool. This work offers practical experience in geospatial AI and traffic modeling while advancing outcomes that enhance mobility, reduce congestion, and support sustainability goals. I am seeking motivated senior undergraduate students with some experience in data analysis or (AI) modeling, but enthusiastic students eager to learn are also welcome to apply.
High Speed Video Analysis Using AI Segmentation:
Project Title: High Speed Video Analysis Using AI Segmentation
| Term: | Fall 2025/Winter 2026 |
Professor: | Mendez, Patricio |
Email: | ccwj@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: As computer vision technology rapidly advances, it becomes an increasingly powerful tool for process monitoring and research, especially in the welding industry. This project aims to apply the power of deep learning to analyze high-speed videography of gas metal arc welding (GMAW)—a critical and widely adopted welding process, included in new applications like additive manufacturing. Throughout this project, you will gain domain knowledge in the field of welding, while gaining insight into how deep learning is transforming our understanding of the complex physical phenomena within the welding arc. You will have the opportunity to refine and enhance the training of the algorithm (written in python for machine learning based on PyTorch) designed to segment images and extract precise, quantifiable data from welding videos. This hands-on experience will not only strengthen your skills in deep learning and computer vision but also provide you with a unique perspective on how these technologies are driving innovation in industrial applications.
High-Resolution Air Quality Prediction via Machine Learning:
Project Title: High-Resolution Air Quality Prediction via Machine Learning
| Term: | Winter 2026 |
Professor: | Zhong, Lexuan (ICE 10-215) |
Email: | lexuan.zhong@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: This project applies machine learning methods to predict high-resolution air quality conditions. The student will:
1. Conduct controlled experiments to generate precise measurements using advanced instruments.
2. Integrate public datasets (e.g., PM₂.₅, wind speed, temperature) from government sources to build and validate predictive models that support modern air quality management and control.
Preferred Skills:
Familiarity with Python is preferred.
Machine-learning based modeling of high-entropy alloys:
Project Title: Machine-learning based modeling of high-entropy alloys
| Term: | Fall 2025/Winter 2026 |
Professor: | Stroberg, Wylie |
Email: | stroberg@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: High-entropy alloys are a promising class of materials that consist of many elements in approximately equal concentrations. They have been shown to outperform traditional allows in a range of areas, including increased strength and toughness, superior wear resistance at high temperatures, and enhanced electrical properties. However, the mechanism(s) for the exceptional performance of these materials is still not well understood. A major challenge in the field is to predict how changes in the composition of the alloying elements affects material properties. This project combines machine learning with molecular-scale simulations to develop an efficient method for stress-strain prediction as the composition of the material is varied.
Machine-Learning–Based Analysis of Indoor Air Quality Dynamics to Inform Air Purification Technology Selection:
Project Title: Machine-Learning–Based Analysis of Indoor Air Quality Dynamics to Inform Air Purification Technology Selection
| Term: | Winter 2026 |
Professor: | Zhong, Lexuan (ICE 10-215) |
Email: | lexuan.zhong@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: This DRA project will use the indoor pollution dataset to develop and validate machine-learning models that capture the coupled, non-linear relationships among occupant behavior, ventilation conditions, seasonal variability, indoor locations, and time-dependent exposure to key pollutants (e.g., PM, CO₂, VOC proxies). The student will train and interpret ML models to quantify how pollutant concentrations and exposures evolve across realistic indoor scenarios, rather than as static averages. The validated models will then be used as a decision-support tool to identify dominant pollutant drivers and co-occurrence patterns, thereby informing the next research phase on which air purification technologies are most appropriate, where they should be deployed, and how they should be operated under different indoor scenarios.
Manufacturing of 3D Lattice Structures by Hybrid Investment Casting:
Project Title: Manufacturing of 3D Lattice Structures by Hybrid Investment Casting
| Term: | Fall 2025 |
Professor: | Henein, Hani (12-219 DICE Building) |
Email: | hani.henein@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: In recent years, 3D printing has become an excellent alternative to casting, especially for making complex shape components that are traditionally, manufactured by investment casting. However, 3D printing, despite covering a wide range of metals and alloys is relatively expensive for manufacturing complex shapes, and the surface finish of the printed part does not always meet the quality specifications. In this work, an economic manufacturing process, termed the hybrid investment casting, is proposed. It combines the traditional investment casting with Stereolithography (SLA) 3D printing. The process consists in creating a 3D model of the part to be manufactured, by selectively curing a polymer resin layer-by-layer using an ultraviolet (UV) laser beam. The model is then used as a pattern for the investment casting of the part.
Measurement and analysis of the optical properties of organic semiconductor thin films:
Project Title: Measurement and analysis of the optical properties of organic semiconductor thin films
| Term: | Fall 2025/Winter 2026 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: The Shankar Lab is collaborating with Canadian industry to fabricate and test optoelectronic devices based on new organic semiconductors which have applications in displays (e.g. OLEDs), solar energy conversion (OPV) and flexible/wearable electronics. A key metric to measure for any new organic semiconductor is the permittivity function. The relative permittivity (also known as dielectric constant) of a material is a complex function of frequency with both real and imaginary components at each frequency. If the relative permittivities of thin organic semiconductor films are quantified over the entire ultraviolet, visible and near-infrared spectral range, the optical and electronic properties of the organic semiconductor are determined to a large extent. The hands-on component of this project involves mastering an instrument known as an ellipsometer which uses polarized light to collect permittivity data. The data analysis component of this project is math-intensive.
Methane Emissions Modelling:
Project Title: Methane Emissions Modelling
| Term: | Fall 2025 |
Professor: | Leung, Juliana (DICE 6-285) |
Email: | juliana2@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: This project focuses on applying image analysis and machine learning techniques for
modelling methane emissions. We aim to better understand emission source characteristics. Students will be applying machine learning and other data-driven approaches to analyze a variety of data, e.g., satellite data. The goal is to develop models or relationships between various source and environmental parameters and emissions characteristics. The student will be working alongside a PhD student on this project. Applicants with a strong programming background/interest will be preferred.
Nanogels for Encapsulation and delivery of potent cancer therapeutic drugs:
Project Title: Nanogels for Encapsulation and delivery of potent cancer therapeutic drugs
| Term: | Fall 2025/Winter 2026 |
Professor: | Narain, Ravin |
Email: | narain@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: This project will involve the synthesis of novel responsive nanogels via the reversible addition-fragmentation chain transfer polymerization for the encapsulation and controlled release of cancer therapeutic drugs. A range of techniques will be used to characterize the nanogels as well as the encapsulation/release of cancer therapeutics.
Nanoscale fluid flow simulations:
Project Title: Nanoscale fluid flow simulations
| Term: | Fall 2025/Winter 2026 |
Professor: | Stroberg, Wylie |
Email: | stroberg@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: At the nanoscale, fluid flow differs from macroscopic flows in that the effects of interfaces becomes the dominant force. This allows for unique device design that takes advantage of interfacial interactions to manipulate fluids and transport nanoparticles. In this project, the student will use molecular dynamics simulations to study the loading of therapeutic nanoparticles into nanometer sized pores. This work will provide new insight into how to fabricate and optimize advanced drug delivery devices.
Optoelectronic properties of doped carbon nitrides:
Project Title: Optoelectronic properties of doped carbon nitrides
| Term: | Winter 2026 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: Carbon nitride (g-C3N4) is a 2-dimensional sheet-like material which is a cousin of graphene. Unlike graphene which is quasi-metallic, C3N4 is semiconducting which enables it to be used as the "active layer" in optoelectronic devices such as diodes, solar cells, photocatalysts and transistors. 2 limitations of conventional C3N4 are its wide electronic bandgap and low electron mobility. The Shankar Lab is working to synthesize high mobility, narrow bandgap carbon nitrides through doping and framework modification. This project will explore 2 different methods to synthesize doped carbon nitrides: (1) Electropolymerization and (2) Thermal polycondensation. Subsequently, the optoelectronic properties of the synthesized carbon nitrides will be measured using UV-vis spectroscopy, photoluminescence spectroscopy, current-voltage measurements, etc. This project is well-suited to students passionate about materials science.
Physics-Informed Machine Learning Models for Radio Wave Propagation:
Project Title: Physics-Informed Machine Learning Models for Radio Wave Propagation
| Term: | Fall 2025/Winter 2026 |
Professor: | Zhang, Xingqi (DICE 11-381) |
Email: | xingqi.zhang@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: This project aims to explore a data-driven approach that can lead to computationally efficient, high-fidelity radio wave propagation models, without performing a channel simulation, by leveraging advances in deep learning (e.g., physics-informed neural network). The goal is to build a learning-centred methodology that can recognize the signal propagation and fading characteristics of a channel over a frequency bandwidth, by processing the channel geometry.
Pilot demonstration of micro-aeration for anaerobic digestion:
Project Title: Pilot demonstration of micro-aeration for anaerobic digestion
| Term: | Fall 2025 |
Professor: | Dhar, Bipro |
Email: | bipro@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: This project focuses on the pilot-scale development and optimization of micro-aeration technology for in-situ biogas desulfurization in anaerobic digestion. Biogas from digesters typically requires costly purification before use in combined heat and power systems or upgrading to renewable natural gas. A major challenge is the presence of hydrogen sulfide that corrodes pipelines and damages equipment. Because of the high costs of conventional end-of-pipe desulfurization, many facilities flare biogas, causing substantial greenhouse gas emissions. Micro-aeration, which involves controlled injection of small amounts of air or oxygen directly into digesters, has emerged as a promising, low-cost alternative. Building on successful bench-scale testing, this project aims to refine process design, monitoring, and maintenance. The outcome will improve biogas quality, reduce flaring, and provide valuable insights for broader application in Canada’s waste and wastewater sectors.
Predicting Drop Impact Outcomes Using Machine Learning:
Project Title: Predicting Drop Impact Outcomes Using Machine Learning
| Term: | Fall 2025/Winter 2026 |
Professor: | Tsai, Amy |
Email: | peichun.amy.tsai@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: In this DRA project, the student will apply machine learning techniques to predict the transitions of impact outcomes during droplet impact. The skills gained include basic literature review, data preprocessing, model selection, and hands-on coding.
Robotic Metal 3D printing of Lattice Structures:
Project Title: Robotic Metal 3D printing of Lattice Structures
| Term: | Fall 2025/Winter 2026 |
Professor: | Qureshi, Ahmed Jawad (10-361 DICE) |
Email: | ajquresh@ualberta.ca |
department: | Mechanical Engineering |
Brief Description: This project will investigate 3D printing of large scale metal lattice structures using robotic directed energy deposition system. Lattice structures are repeated patterns that fill a volume or conform to a surface. In engineering design, lattices are cellular materials—often inspired by nature—that consist of beams, surfaces, or plates that fit together following an ordered or stochastic pattern.
The main activities will be:
- Designing components in Solidworks, or Ntopology
- Prototyping and Manufacturing with 3D printing
- Running design of experiments for characterization and optimization
The required skills are:
- Proficiency with Solidworks modeling. CSWA preferred
- Experience in programming, ideally with good skills in python, or C/C++
Semantic Segmentation of Urban LiDAR Data for Infrastructure Analysis:
Project Title: Semantic Segmentation of Urban LiDAR Data for Infrastructure Analysis
| Term: | Fall 2025 |
Professor: | El-Basyouny, Karim |
Email: | basyouny@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: Our research develops innovative models and algorithms to improve the safety of both human drivers and autonomous vehicles. This project combines advanced sensors with data-processing tools for the automatic detection and extraction of road and roadside features. A key focus is the use of machine learning for semantic segmentation of 3D point cloud data, emphasizing LiDAR in transportation and highway engineering. The student will contribute to algorithm and code development, as well as manual annotation of point clouds, to automate computations on labeled data for extracting quantitative measures of rodway geometric conditions. The role provides hands-on experience in semantic segmentation, ML frameworks, and model optimization. Prior experience with LLMs, Transformer architectures, and coding is beneficial, but motivated students with strong interest are encouraged to apply.
Simulating the hopping and trapping dynamics of bacteria in porous media:
Project Title: Simulating the hopping and trapping dynamics of bacteria in porous media
| Term: | Fall 2025/Winter 2026 |
Professor: | Peng, Zhiwei (ICE 12-324) |
Email: | zhiwei.peng@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: Various processes such as microbial drug delivery and bioremediation rely on bacteria migration in porous media. Recent experimental work shows that bacteria move through pore spaces in a non-trivial fashion. Using simple computer simulations, we will study the migration behavior of motile bacteria in porous media. We will characterize their migration pattern and diffusion dynamics in relation to the porous media structure and the bacteria's swim speed. These results will reveal the physics of bacteria migration in complex environments and provide guiding principles towards application. This project is ideal for students who have an interest in fluid mechanics, colloidal systems, and/or computer simulation.
Synthesis, characterization and electrical testing of core-shell chalcogenide quantum dots:
Project Title: Synthesis, characterization and electrical testing of core-shell chalcogenide quantum dots
| Term: | Fall 2025 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: The Shankar Lab has strong expertise in the synthesis & application of brightly fluorescent nanoparticles made of various semiconductors. One family of particular interest consists of the inorganic chalcogenides such as CdS, CdSe, PbS and CdTe. Quantum mechanical size confinement renders the optical properties of the nanoparticles tunable, hence the term quantum dots (QDs). The core-shell structure (e.g. CdSe@CdS) passivates defect states and allows excellent control over the optoelectronic properties of quantum dots. The objectives here are two-fold. First, to use sustainable synthetic protocols to achieve quantum dots with a narrow size dispersion. Second, to perform spectroscopic and electrical characterization of the quantum dots.
Testing Composite Materials for 100-Year Water Infrastructur:
Project Title: Testing Composite Materials for 100-Year Water Infrastructur
| Term: | Winter 2026 |
Professor: | Abtahi, Shaghayegh (DICE 6-215) |
Email: | abtahi1@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: Looking to build your lab skills and work on real-world engineering research? Join our team to study Cured-in-Place Pipe (CIPP) liners, a cutting-edge technology used to repair aging water pipes without digging up streets. Our goal is to understand how these composite materials perform under constant pressure so they can be designed to last 100 years.
As part of the project, you will help graduate students with creep-rupture experiments, specimen preparation, and load application, while also gaining experience in image processing and Digital Image Correlation (DIC) to track how materials deform over time. This is a unique chance to combine hands-on lab work with computer-based analysis, develop technical skills valued in both research and industry, and contribute to solutions that make Canada’s infrastructure more sustainable and resilient.
Testing Hydrogen Solubility in Brine for Subsurface Energy-Storage Applications:
Project Title: Testing Hydrogen Solubility in Brine for Subsurface Energy-Storage Applications
| Term: | Fall 2025 |
Professor: | Dehghanpour, Hassan |
Email: | dehghanpour@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: Hydrogen energy is considered a key fuel towards a sustainable future, and this project puts you at the forefront of hydrogen storage innovation. Join researchers in examining hydrogen storage in salt caverns with active field sampling from Alberta’s Lotsberg Salt Formation! Salt caverns are artificially made through solution mining in thick salt rock deposits, and can be up to 200,000 m3 in volume. When hydrogen is stored in salt caverns, it may be in contact with highly saline brine that can host hydrogenotrophic (hydrogen-eating) microorganisms. Hydrogenotrophs consume hydrogen and produce unwanted byproduct gases which compromise hydrogen purity. In this project, you will monitor hydrogen solubility into brine with active microorganisms at reservoir conditions to determine how hydrogenotrophs contribute to hydrogen solubility. You will also contribute to enrichment culture experiments and expand your passion for interdisciplinary sciences. The outcome of this project will contribute to ongoing research on subsurface hydrogen energy storage that has real-time industry influence.
The role of Fe in recycled Al alloys:
Project Title: The role of Fe in recycled Al alloys
| Term: | Fall 2025/Winter 2026 |
Professor: | Henein, Hani (12-219 DICE Building) |
Email: | hani.henein@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: The goal towards net zero is driving the demand for more metals and alloys. For example, increased electrification of our transport systems demands more aluminum alloys to keep vehicles light. Aluminum being a critical material in Canada, leads to the need to increase the recycling of this precious commodity. Doing so will result in an increase in the iron content of aluminum alloys. Iron being more noble than aluminum cannot be chemically removed from the alloy. Thus, the aluminum alloy has to be processed with iron in the recycled alloys. Unfortunately, iron forms an intermetallic compound with aluminum, Al13Fe4 which is detrimental to the properties of the alloy dur to its acicular shape. There have been thousands of research efforts made to better understand the formation of this intermetallic as well as to explore alloying techniques to mitigate the effect of iron in aluminum alloys. No solution presently exists. The goal of this project is to use AI or ML to search the literature and generate a summary of the efforts that have been explored in this area and to define new approaches in seeking a solution.
TRMC measurements using organic semiconductor loaded microwave resonators:
Project Title: TRMC measurements using organic semiconductor loaded microwave resonators
| Term: | Fall 2025 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: The goal of this project is to measure the S-parameters of planar microwave resonators loaded with thin films of novel organic semiconductors. The Shankar Group is using high frequency (i.e. microwave) devices and circuits for sensing and diagnostics; a tremendous amount of data is being generated which needs to processed. ECE students are typically aware of Z-parameters, Y-parameters, h-parameters and ABCD parameters used to describe the behavior of low frequency circuits and systems. At GHz and higher frequencies, S-parameters are used. Interested students will be proficient users of python and/or MATLAB, which will be needed to process the data and convert them into scientifically meaningful graphs/plots followed by curve fitting. Some knowledge of machine learning is desirable but not essential. This project provides great introductory hands-on experience in microwave engineering and handling Big Data.
Ultrahigh Molecular Weight Polymers:
Project Title: Ultrahigh Molecular Weight Polymers
| Term: | Fall 2025/Winter 2026 |
Professor: | Narain, Ravin |
Email: | narain@ualberta.ca |
department: | Chemical and Materials Engineering |
Brief Description: This project will focus on the development of ultra-high molecular weight polymers (UHMW-Polymers). UHMW-Polymers can be used in a wide range of industrial applications, such as in water treatment, environmental remediation, detergents, oil field, and drag reduction. These polymers are known to perform as stabilizers, thickeners, dispersing agents, dewatering aids, viscosity modifiers, film formers, and flocculants. The research will involve the design of custom made polymers by vertical polymerization process and their characterizations.
Video Analytics of Livestock Animals:
Project Title: Video Analytics of Livestock Animals
| Term: | Fall 2025 |
Professor: | Cheng, Li (ICE, 11-365) |
Email: | lcheng5@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: This project focuses on analyzing the visual motion of livestock animals from video feeds, in collaboration with Dr. Anne Laarman, an animal science expert in the Faculty of Agricultural, Life, and Environmental Sciences. The work has many exciting downstream applications—for example, predicting the timing of calf delivery based on the motion patterns of the mother cow. As part of this project, you will work closely with a graduate student or researcher, become familiar with state-of-the-art deep learning and computer vision methods, and gain hands-on research experience in this interdisciplinary area.
Visual Motion Analysis from Images and Videos:
Project Title: Visual Motion Analysis from Images and Videos
| Term: | Fall 2025/Winter 2026 |
Professor: | Cheng, Li (ICE, 11-365) |
Email: | lcheng5@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: This project focuses on analyzing visual motions of human and animals from video feed, a problem that plays a crucial role in many real-life applications ranging from natural user interface to autonomous driving. You are expected to work with a graduate student/researcher, get familiar with state-of-the-art deep learning techniques, and gain hands-on research experience on benchmark and home-grown datasets.
Wetting and imbibition in SAM-coated metal oxide nanopores:
Project Title: Wetting and imbibition in SAM-coated metal oxide nanopores
| Term: | Fall 2025/Winter 2026 |
Professor: | Shankar, Karthik (11-384 DICE Bldg) |
Email: | kshankar@ualberta.ca |
department: | Electrical and Computer Engineering |
Brief Description: Nanoporous metal oxides have a broad range of applications ranging from biosensors to energy storage & conversion devices such as batteries, supercapacitors and solar cells. Most of the aforementioned device applications involve interfaces between dissimilar materials e.g. metal oxide and polymer OR metal oxide and noble metal OR metal oxide and chalcogenides. Such dissimilar materials typically fail to achieve intimate contact, a problem that is exacerbated in nanopores due to challenges with pore infiltration. Interface-modifying self-assembled monolayers (SAMs) offer a solution by acting as a buffer layer that interacts strongly with both components of a heterojunction. This project will coat nanoporous metal oxides with SAMs and measure the resulting wetting and imbibition dynamics when infiltrated with polymers and metals. This project mainly concerns microfluidics, which is a sub-discipline of mechanical engineering, but interested students from any major are welcome to apply.
Yukon river ice study:
Project Title: Yukon river ice study
| Term: | Winter 2026 |
Professor: | Loewen, Mark |
Email: | mark.loewen@ualberta.ca |
department: | Civil and Environmental Engineering |
Brief Description: This research project aims to investigate river ice processes in a small-steep-regulated river in Yukon during the 2023-2024 winter season. Frequent overflow events cause thick layers of aufeis to form on the floodplain. Aufeis is layered ice that forms on the ground surface or on top of existing river ice when successive flows of water freeze in sub-zero temperatures. Temperature loggers were deployed on staff gauges at different locations on the floodplain where aufeis forms. Research will include plotting and analysis of time series of the vertical profiles of temperature time series collected at each location; and correlation analysis of hydrometeorological data including water levels, air temperatures and solar radiation. The results will help explain when and why aufeis grows on floodplains and impacts heat exchange between air, ice, and ground. These findings can be used to guide monitoring strategies and to improve/validate river-ice and aufeis process models used in cold-region hydrology.
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