From: jobs at ccl.net (do not send your application there!!!)
To: jobs at ccl.net
Date: Thu Aug 8 14:50:02 2019
Subject: 19.08.08 Computational discovery of new energy materials, UToronto, NRC, CMU
Computational discovery of new energy materials: A collaboration between
the University of Toronto, the National Research Council of Canada, and
Carnegie Mellon University
Are you looking to work at the forefront of energy materials discovery?
Successful candidates will have the opportunity to work with two
world-leading theorists and an experimentalist at three outstanding
institutions. They will solve real-world problems and simultaneously
deepen their computational expertise. We are looking for driven,
innovative researchers to work on projects that lever leadership in deep
learning and outstanding supercomputer resources to discover new
materials for renewable energy (storage and generation).
- Position details:
- The candidates will work collaboratively among groups at the NRC, the
University of Toronto, and Carnegie Mellon University. This is an
opportunity to combine expertise, methods, and datasets.
- Based on expertise, positions available, and geographic
considerations, the candidates will hold a full-time appointment at one
of the three institutions (NRC, UT, CMU), and will engage regularly
online and in-person in addition.
- Ideally, candidates will have:
- Expertise in computational chemistry and physics such as:
- The application of quantum chemistry methods to investigate
reaction mechanisms and structure-property relations in electrocatalysis
- And/or the application of quantum chemistry and band structure
methods to calculate crystal and electronic structures of semiconductor
- Expertise in high-throughput DFT: familiarity with developing,
running, and analyzing calculations to explore wide chemical spaces and
have significant experience in python and working in a linux development
- Strong written and oral communication skills, time management skills,
and desire to work in research teams.
- Additional skills:
- Experience with code sharing and documentation for collaboration
projects. Ideally with code samples or published projects on resources
like Github, Bitbucket, etc.
- Familiarity with machine learning techniques such as traditional
feature-based machine learning, graph convolution methods, deep learning,
image recognition, generative models, reinforcement learning, NLP.
Comfort with at least one machine learning framework (pytorch,
tensorflow, keras, etc).
- Interest in collaborating with an experimental team to apply insights
obtained using ML + DFT to the discovery and validation of new catalysts
and new semiconductors.
Please apply with a cover letter, your CV, and the names of three
referees. Application packages should be emailed to each of:
NOTE THAT E-MAIL ADDRESSES HAVE BEEN MODIFIED!!!
- Ted Sargent – ted.sargent:+:utoronto.ca
- Isaac Tamblyn – Isaac.Tamblyn:+:nrc-cnrc.gc.ca
- Andrew Johnston – ak.johnston:+:utoronto.ca
- Zachary Ulissi – zulissi:+:andrew.cmu.edu
All @ signs were changed to :+: to fight spam. Before you send e-mail, you
need to change :+: to @
For example: change joe:+:big123comp.com to firstname.lastname@example.org
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