Our group is a dynamic research team focused on advancing the understanding of many-body dynamics and non-equilibrium systems through the lens of Dynamic Density Functional Theory (DDFT). We specialize in applying artificial intelligence (AI) techniques to design, analyze, and explore advanced materials across various states, including condensed matter, liquids, solids, and gases. By leveraging cutting-edge AI algorithms and computational tools, we aim to accelerate materials discovery, optimize material properties, and unravel the complex mechanisms that govern material behavior in non-equilibrium conditions.
Current opening
Position 1 (MSc/PhD): Modeling Interfacial Reaction-Diffusion in Electrochemical Systems
We seek a highly motivated candidate to develop dynamic density functional theory (DDFT)-based models of interfacial reaction-diffusion processes in electrode systems. This work will focus on transport phenomena at the electrolyte-electrode interface and applications in corrosion prevention, electric double-layer capacitors, and advanced electrolyte design. The ideal candidate has experience in computational physics/chemistry, partial differential equations, and machine learning. Familiarity with DFT, DDFT, or electrochemical modeling is highly desirable.
Position 2(MSc/PhD) : Modeling Tissue Dynamics Using DDFT
We are looking for a candidate to develop dynamic density functional theory, DDFT-based models of tissue dynamics, incorporating features such as proteins and membrane deformations. Using physics-informed machine learning, this project will explore nanoscale biological processes, including intracellular signaling and tissue morphogenesis. Applicants should have a background in computational biology, biophysics, or applied mathematics, with experience in PDEs, multiscale modeling, or molecular dynamics simulations.
Qualifications:
- Strong programming skills (e.g., Python, C++, or Julia).
- Experience with computational modeling frameworks and machine learning tools.
- Demonstrated ability to work independently and collaboratively.
If interested, please submit your CV, a brief statement of research interests, and references to cgtetsas@uwaterloo.ca .
Positions available at the Feugmo Research Group
If you are interested In Machine Learning applied to Materials Science or you have a strong background mathematics or theoretical or computational physical science (chemistry, physics or materials), please reach out to me.
Feugmo Research Group
Department of Chemistry
University of Waterloo
200 University Ave. West
Waterloo, ON N2L 3G1
Canada
Group Leader
Conrard Giresse Tetsassi Feugmo
Assistant Professor
Office: PHY 2018
Email: cgtetsas@uwaterloo.ca
Phone: 519-888-4567 x33024