Application of neural network or deep learning algorithms in pattern recognition
This project aims to build deep machine learning models (e.g. recurrent neural networks, transformer models, etc.) to perform experimental data analysis where tasks of classification and pattern recognition are performed.
Required knowledge & skills: Python, machine learning algorithms.
Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Location: Remote/RAC-2101
Application of quantum and classical machine learning algorithms in real-life data analysis
This project aims to build quantum and classical deep machine learning models (e.g., recurrent neural networks, transformer models, etc.) to assess real-life big data, where tasks such as classification, anomaly detection, and pattern recognition are performed.
Required skills: Python, machine learning algorithms.
Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Phone: 519-888-4567 x30481
Location: Remote/RAC-2101
Comparing the accuracy of bone density measurements obtained from a dual energy chest radiograph to a state-of-the-art DEXA scan
DEXA is the established gold standard when it comes to bone density measurements. However, it is a test that requires specialized equipment and is prescribed for a very targeted population e.g. seniors. In contrast, X-ray imaging is a commonly prescribed imaging test in outpatient clinics and emergency departments across Canada. This project will investigate the difference in error between a bone density measurement extrapolated from a dual energy X-ray image and compare it to the measurement obtained from a Dual Energy X-ray Absorptiometry (DEXA) scan.
Supervisor: Prof. Karim S. Karim
Email: kkarim@uwaterloo.ca
Data Denoising and Preprocessing for Scientific Applications
This project aims to apply and adapt existing denoising algorithms to scientific datasets, such as medical or chemistry data. The goal is to improve data quality and prepare it for subsequent analysis in larger research pipelines. Students will start by using existing Python code for denoising (e.g., for 1D signals or 2D images) and are encouraged to explore alternative preprocessing methods to achieve higher-quality results. This project provides hands-on experience in data handling, preprocessing, and experimental validation within real scientific workflows.
Required knowledge & skills: Basic Python programming; script automation; familiarity with data processing or machine learning libraries; interest in scientific or medical data analysis.
Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Phone: 519-888-4567 x30481
Location: Remote/RAC-2101
Development of high performance nanomaterial-based energy harvesting devices for self-powered applications
The objective of this project is to develop high performance energy harvesting devices based on novel nanomaterials, which can convert mechanical energy to electrical energy, for self-powered electronic and sensing applications. Working with a group of experienced postdoctoral fellow and graduate students, the student will be trained on nanomaterial synthesis, nanomaterial characterization, device microfabrication and characterization, data analysis and technical writing.
Required knowledge & skills: hands-on capability
Supervisor: Prof. Dayan Ban
Email: dban@uwaterloo.ca
Electrical Characterization Studies on Power Cables used in Nuclear Power Plants
Research Description: Research Description: The work will consist of electrical characterization studies on long samples (i.e. 3 to 10 meters) prepared from medium voltage (MV) polymeric cables utilized in power generation and distribution applications. Students will have the opportunity to learn different, efficient, and practical characterization methods of power cables which are used in industry. These methods include but not limited to Dielectric Spectroscopy (DS), Polarization and Depolarization Current (PDC) measurements, Time- and Frequency- Domain Reflectometry (TDR and FDR) tests, high-frequency dielectric characterizations.
Supervisor: Prof. Shesha Jayaram
Email: shesha.jayaram@uwaterloo.ca
Numerical Simulations of nanoelectronic devices
This project aims to perform numerical simulations of nanoelectronic devices based on nanomaterials, which will explain experimental device performance.
Required knowledge & skills: Semiconductor device physics
Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Location: Remote/RAC-2101
Performance of EV Motors with High-Voltage Wide Band Gap Drives
The step-up transformers in the windfarms are prone to premature failure due to high-frequency repetitive transients generated by the operation of power electronics switching and frequent circuit breaker operations. In this work, transformer insulation degradation under repetitive transient voltages will be evaluated. Partial discharge inception voltage, intensity of discharges, time to failure and the rate of hydrogen gas generation are the parameters considered to evaluate the level of degradation under different operating conditions.
Supervisor: Prof. Shesha Jayaram
Email: shesha.jayaram@uwaterloo.ca
Windfarms connected transformer insulation under repetitive transient voltages
Electric Vehicles (EVs) are rapidly evolving with higher operating voltages, hairpin winding motors, and wide-bandgap (WBG) power converters that enable faster charging and extended driving range. While these advancements enhance performance, they also introduce significant challenges for motor reliability, particularly in the turn-to-turn insulation system of stator windings. High-frequency, fast-switching pulses generated by WBG-based pulse-width modulation (PWM) drives impose greater electrical stress, increasing the risk of partial discharge (PD)activity and accelerating insulation degradation. This project focuses on characterizing and evaluating the performance of special type of winding insulation used in EVs under high-voltage WBG drive conditions.
Supervisor: Prof. Shesha Jayaram
Email: shesha.jayaram@uwaterloo.ca