MSc/PhD Positions in Computational Modeling: The University of Waterloo is offering MSc/PhD positions for highly motivated candidates interested in computational modeling. These positions focus on dynamic density functional theory (DDFT) applied to electrochemical systems and biological tissue dynamics. Successful applicants will work on interdisciplinary research projects involving machine learning, partial differential equations (PDEs), and multiscale modeling.
Summary Table
| Position | Research Focus | Preferred Background |
|---|---|---|
| MSc/PhD Position 1 | Modeling Interfacial Reaction-Diffusion in Electrochemical Systems | Computational physics/chemistry, PDEs, machine learning, DFT/DDFT, electrochemical modeling |
| MSc/PhD Position 2 | Modeling Tissue Dynamics Using DDFT | Computational biology, biophysics, applied mathematics, PDEs, multiscale modeling, molecular dynamics |
Study Area
- Computational Physics
- Computational Chemistry
- Biophysics
- Applied Mathematics
- Computational Biology
Scholarship Description
The selected candidates will develop DDFT-based models for either interfacial reaction-diffusion processes in electrochemical systems or tissue dynamics incorporating nanoscale biological processes. These projects involve physics-informed machine learning techniques and numerical modeling approaches, contributing to advancements in corrosion prevention, electrolyte design, and intracellular signaling.
Eligibility
- MSc or PhD applicants with a background in computational physics, chemistry, biology, biophysics, or applied mathematics
- Strong programming skills (Python, C++, or Julia)
- Experience with computational modeling frameworks and machine learning tools
- Ability to work independently and collaboratively
Required Documents
- CV
- Brief statement of research interests
- References
How to Apply
Interested candidates should submit their application materials via email to cgtetsas@uwaterloo.ca.
Last Date
Applications will be reviewed on a rolling basis until the positions are filled. Early applications are encouraged.
