Postdoc in Deep Learning Surrogate Models for Multiscale Simulation of Electrocatalytic Interfaces for P2X - DTU Energy

This two-year postdoc position is a part of the T3: Grid2Atoms – Atoms2Grid: A Chemistry-Aware Digital Twin in the Pioneer Center for Accelerating P2X Materials Discovery (CAPeX).
The Section for Autonomous Materials Discovery (AMD) at DTU Energy and the Pioneer Center for Accelerating P2X Materials Discovery (CAPeX, www.capex-p2x.com) are looking for an outstanding postdoc to lead the effort in longer time and length scale simulation of electrocatalytic Interfaces and development of ground breaking understanding of catalytic processes at the interfaces.

Project Descriptions
The AMD section is a leading group combining atomic- and multiscale modeling techniques, machine learning methods, and self-driving laboratories to accelerate the discovery of novel materials for energy conversion and storage. The fellowship is part of the large-scale and long-term Pioneer Center for Accelerating P2X Materials Discovery (CAPeX). CAPeX is a 13-year and 300 MDKK “Pioneer Center for Accelerating P2X Materials Discovery”, funded by the Danish Ministry of Higher Education and Science, the Danish National Research Foundation, the Carlsberg Foundation, the Lundbeck Foundation, the Novo Nordisk Foundation, the Villum Foundation. CAPeX is hosted by the Technical University of Denmark (DTU) and co-hosted by Aalborg University (AAU) and unites leading experts from DTU, AAU, University of Copenhagen, Aarhus University, and the University of Southern Denmark with international partners from Stanford University, Utrecht University, and the University of Toronto to form a truly Pioneering Center on P2X. You can read more about our organization’s CAPeX research themes and X-trails at www.capex-p2x.com.

Responsibilities and qualification
Working closely with others you will develop new graph neural network architectures and datasets with physics simualtions that will allow us to create fast digital twins of multiscale reactive dynamics of solid-liquid interface at the catalyst surfaces. You will also work on generative models akin to Alphafold3 to sample states that are hard to reach with simulations to elucidate mechanisms with never before clarity.

Specifically, you will (a) Improve on existing equivariant graph neural network models and train those as surrogate simulators for catalyst-electrolyte interfaces using GPU HPCs. (b) deploy such ML accelerated simulations at longer time and length scale for unveiling of novel scientific insights on electrochemical reactions (c) collaborate with top industrial research laboratories and academic groups to jointly build universal foundational graph network models for solid-liquid interfaces (d) explore generative modelling for accelerated statistical sampling.

Candidates should hold a PhD or equivalent degree in computational chemistry, physics, materials, or computer science. The candidates must have a strong background in computational methods like density functional theory and/or machine learning and sufficient Python programming skills to implement new methods/models needed for the project. You are expected to have performed original scientific research within the relevant fields listed above. Moreover, you:

• are innovative and able to work both independently and in cross-disciplinary teams,
• have excellent communication skills in English, both written and spoken,
• can work independently and take responsibility for the progress and quality of projects.

Assessment
The Head of the Section, Director of CAPeX, Prof. Tejs Vegge, and Associate Prof. Arghya Bhowmik (DTU) will assess the applicants.

We offer
DTU is a leading technical university globally recognized for its excellence in research, education, innovation, and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and terms of employment
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union.

The period of employment is two years. The employment is expected to start on January 1st, 2025 or shortly after that. The position is a full-time position.

You can read more about career paths at DTU here.
Further information
Please contact Prof. Tejs Vegge at [email protected] or Prof Arghya Bhowmik at [email protected] if you need further information concerning these positions.

Please do not send applications to these e-mail addresses, instead, apply online as described below.

If you are applying from abroad, you may find helpful information on working in Denmark and at DTU at DTU – Moving to Denmark.

Application procedure
Your complete online application must be submitted no later than 15 October 2024 (23:59 Danish time).

Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now,” fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

• Application (cover letter)
  • in your application, please reply to the following
    • what is your experience with atomic-scale machine learning models and quantum mechanical simulation of catalyst surfaces and interfaces?
• CV
• Academic Diplomas (MSc/PhD – in English)
• List of publications
• A short highlight of your academic achievements (half page)
• List of References (at least two)

Applications received after the deadline will not be considered.

All interested candidates, irrespective of age, gender, disability, race, religion, or ethnic background, are encouraged to apply.

The Department of Energy Conversion and Storage (DTU Energy) focuses on research and development of functional materials, components, and systems for sustainable energy technologies. The technologies include fuel cells, electrolysis, power-to-x, batteries, and carbon capture. The research is based on strong competences on electrochemistry, atomic scale and multi-physics modelling, autonomous materials discovery, materials processing, and structural analyses. We also focus on educating engineering students at all levels, ranging from BSc, MSc, PhD to lifelong learning students. We have about 270 dedicated employees. Read more about us at www.energy.dtu.dk

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DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear mission to develop and create value using science and engineering to benefit society. That mission lives on today. DTU has 13,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.

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