PhD in ML and physics-based modeling for efficient electrocatalyst discovery

DIFFER

The Dutch Institute for Fundamental Energy Research (DIFFER) performs leading fundamental research on materials, processes, and systems for a global sustainable energy infrastructure. We work in close partnership with (inter)national academia and industry. Our user facilities are open to industry and university researchers. As an institute of the Dutch Research Council (NWO) DIFFER plays a key role in fundamental research for the energy transition.

We use a multidisciplinary approach applicable on two key areas, solar fuels for the conversion and storage of renewable energy and nuclear fusion – as a clean source of energy.

Differ is looking for a

PhD Researcher in ML and physics-based modeling for efficient electrocatalyst discovery (X/F/M)

A PhD position is available in the Autonomous Energy Materials Discovery (AMD), a fully computational research group within the Chemical Energy Department at DIFFER. AMD focuses on developing and utilizing automated virtual materials discovery frameworks, driven by high-throughput classical and quantum calculations, artificial intelligence techniques, and advanced data infrastructures, to accelerate the discovery of materials and molecules for energy applications.

This PhD project is part of a grand effort to revolutionize electrocatalysts for green hydrogen production. The aim is to use advanced computational methods to model next-generation electrocatalysts, optimize their performance, and reduce reliance on critical materials. By applying high-throughput simulations and machine learning, this work will accelerate the development of innovative, cost-effective catalysts, contributing to the global energy transition and green hydrogen economy.

Position and requirements

RESPONSIBILITIES

1. Use advanced machine learning models for accelerated electrocatalyst screening.
2. Conduct physics simulations on selected electrocatalyst candidates.
3. Analyze simulation data to gain insights into catalytic performance.
4. Collaborate with researchers from the consortium and DIFFER.
5. Identify and recommend high-potential electrocatalyst materials to collaborators.
6. Supervise projects of junior researchers, with no course teaching duties.
7. Prepare research presentations and publications. 
8. Complete your PhD thesis within four years based on obtained results.

REQUIREMENTS

1. Master’s degree in Computational Science, Physics, Chemistry, Materials Science, Engineering, or a related field.
2. (Preferred) Experience with machine learning (ML) techniques.
3. (Preferred) Experience in atomistic simulations (DFT, MD).
4. (Preferred) Proficiency in Python programming.
5. Strong collaborative skills and ability to thrive in an interdisciplinary team.
6. Excellent written and verbal communication skills in English. 

Terms and conditions

This position is for 1 FTE, will be for a period of 4 years and is graded in pay scale PhD. Starting salary is 2884 EUR in the first year and will increase to 3694 EUR in the fourth year of the employment. The position will be based at DIFFER (www.differ.nl) and the working location will be at TU Eindhoven. When fulfilling a position at DIFFER, you will have an employee status at NWO. You can participate in all the employee benefits NWO offers. We have a number of regulations that support employees in finding a good work-life balance. At DIFFER we believe that a workforce diverse in gender, age and cultural background is key to performing excellent research. We therefore strongly encourage everyone to apply. More information on working at NWO can be found at the NWO website (https://www.nwo-i.nl/en/working-at-nwo-i/jobsatnwoi/)

Information and application

For further information about the position please contact: Dr. Süleyman Er, s.er@differ.nl

Please submit your application via our online portal via the button underneath, including a Cover Letter and CV.

Closing date of this vacancy is November 2. The application interviews will be held in weeks 45 and 46.

Closing date

November 02, 2024