[Job-offers-cs] Postdoctoral Research Associate, High-Performance Parallel Graph-Based Machine Learning

[Pekka Orponen]

-------- Forwarded Message --------
Subject: [SIAM-OPT] Postdoctoral Research Associate, High-Performance 
Parallel Graph-Based Machine Learning
Date: Tue, 29 Sep 2020 22:41:18 -0400
From: Kimon Fountoulakis <kfountou at uwaterloo.ca>
To: Pekka Orponen <pekka.orponen at aalto.fi>
CC: Optimization SIAG mailing list <siam-opt at siam.org>

We are looking for a postdoctoral research associate to join our 
research group (opallab.ca) 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. The successful candidate will also work with Dr. Semih 
Salihoglu (member of the Data Systems Research Group) and the Waterloo 
Huawei Joint Innovation Lab 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.

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.

Starting date: January 1, 2021 or later.
Duration: 1 - 3 years
Salary: 90000 CAD per year

Additional information can be obtained by contacting Dr. Kimon 
Fountoulakis by email at kimon.fountoulakis at uwaterloo.ca.
Apply by sending an email to Dr. Kimon Fountoulakis.

Requirements

- 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.