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Up Directory CCL 19.12.12 Postdoc of Computational materials with Machine learning and AI (+DFT,VASP, Battery, MOF)
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Date: Thu Dec 12 11:27:33 2019
Subject: 19.12.12 Postdoc of Computational materials with Machine learning and AI (+DFT,VASP, Battery, MOF)


Postdoc Research Associate Position in Computational Materials with AI and Machine learning

Postdoctoral Research Associate opening is available immediately at Machine Learning and Evolution group at the Department of Computer Science and Engineering, the University of South Carolina (USC). This position will be focused on collaborating with computer science teams to develop machine learning, deep learning and AI based computational approaches for discovery of battery materials and metal organic framework (MOF). It will be jointly supervised by Professor Jianjun Hu (google scholar link) of Computer Science with focus on AI algorithm design and by Professor Ming Hu (google scholar link) who specializes in first-principle calculation (DFT and MD).

Our group develops and employs machine learning and AI algorithms together with DFT/MD based computational modeling and simulation techniques for inverse design and discovery of materials (battery and MOF). Our group collaborates closely with computational and experimental materials research groups at USC (especially the Solid Oxide Fuel Cell center), leading to many opportunities for data and AI driven materials discovery. More information about our group can be found at http://mleg.cse.sc.edu.

Qualifications of the candidate:

  • Ph.D. in Materials Science, Physics, Chemistry or any related field is required.
  • Knowledge of materials science and solid state physics.
  • Experience with ab initio computational methods (DFT via VASP) or MD.
  • Basic scientific programming skills (Python preferred);
  • Experience in electrochemical storage system modeling, preferably in lithium-based batteries or MOF is a plus.

A wide range of machine learning techniques and deep learning models will be developed in our group via close collaboration between the machine learning team and the computational materials team. We encourage applicants with strong interests in AI+ computational materials to apply. We particularly look for candidates who are good at identifying key challenges in current materials discovery and inverse materials design to be addressed by the latest deep learning and machine learning techniques. We like candidates with strong innovation and passion to bridge fundamental scientific inquiry and high-impact applications by working with our strong AI and deep learning team.

A cover letter, curriculum vitae, representative publications, and a list of references should be sent to Prof. Hu at jianjunh[A]cse.sc.edu. A starting date of early 2020 is preferred.

University of South Carolina, is located in Columbia, the capital of South Carolina. Our team and our collaborators have been supported with multiple grants from Federal funding agencies including the Department of Energy (DOE), the National Science Foundation (NSF), the National Aeronautics and Space Administration (NASA), the National Institutes of Health (NIH).

A cover letter, curriculum vitae, and a list of references should be sent to Prof. Hu at jianjunh AT cse.sc.edu
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