We are looking for a postdoctoral research associate to join our research group at the Computer Science department at the University of Waterloo. Our goal is to develop parallel and communication efficient algorithms for large-scale graph-based machine learning.
Example algorithms and applications include but not limited to: optimization algorithms for local graph clustering and optimization algorithms for training graph neural networks for node classification, link-prediction, community detection, graph classification and graph generation.
The successful candidate will also collaborate with the Waterloo-Huawei Joint Innovation Lab and with the end goal to develop a new system or support existing systems on graph-based machine learning. The candidate will also be part of the Scientific Computation Group and the Waterloo Artificial Intelligence Institute.
Starting date: January 1, 2021 or later.
Duration: 1 – 3 years
Additional information can be obtained by contacting Dr. Kimon Fountoulakis by email at firstname.lastname@example.org.
Apply by sending an email to Dr. Fountoulakis following the instructions at https://opallab.ca/vacancies.
PhD, within the last 3 years, in one of the following or other relevant subjects: numerical optimization, scientific computing, parallel computing, applied math, machine learning.
Knowledge of (randomized) first-order numerical optimization algorithms, numerical linear algebra, neural networks or other machine learning models.
Experience in code development and computational experience in using high-performance parallel computing resources. In particular, demonstrated coding experience in C and/or C++, experience in parallel programming using GPUs and/or using tools like MPI and OpenMP. Experience with neural network frameworks such as PyTorch.
Publication record in high-impact journals, top-tier machine learning, and related conferences.
Excellent written and oral communication skills.