BSc/MSc/Internship projects: Virtual materials discovery for energy applications

Please note: unless otherwise specified, the internships are only available for students with a nationality of an EU-member state and/or students from a Dutch university.

DIFFER (Dutch Institute for Fundamental Energy Research) is one of the NWO institutes and focuses on a multidisciplinary approach of the energy research combining physics, chemistry and materials engineering. The institute is an important part of the energy research strategy of NWO. The DIFFER mission is to carry out leading fundamental research in the field of fusion-energy and solar fuels, in close collaboration with academic institutions, research institutes and industry.

BSc/MSc/Internship projects: Virtual materials discovery for energy applications

The discovery of new energy materials is becoming a large-scale challenge that is far beyond the reach of experimentation but also stretching the limits of conventional computation. At DIFFER; we are working on to improve the speed and the prediction power of computation for the discovery of new solar energy conversion and energy storage materials.

We have a variety of projects in the general field of computational materials design for energy applications, and involve finding new solar energy conversion or energy storage materials using the suitable computational tools (e.g. quantum chemistry calculations and machine learning) for the task.

The projects will be carried out under the supervision of Dr. Süleyman Er, and depending on the task they may involve collaborations with researchers from the USA (Harvard) and the Netherlands (Center for Computational Energy Research).

Location

The projects will be carried out at DIFFER, Eindhoven, the Netherlands.

Responsibilities and tasks: 

Depending on your background and your timeframe, your tasks may include either of the following:

  • virtual chemical library generation of candidate materials,
  • performing DFT calculations using common electronic structure software,
  • data analytics of calculation output in the order of 103 to 106 entries, or
  • using calculation output data to train machine learning models for new subgroup materials’ property predictions.
Qualifications: 

We are looking for many computationally skilled, hardworking, and focused (BSc/MSc/Internship) students with background in Computational (Physics/Chemistry/Materials Science), Computer Science, Data Science or equivalent.

Candidates with good programing skills and any level of experience with either of electronic structure calculations, high-performance computing, machine-learning methods or big data analytics will be preferred. Good communication skills (in English) are required.

Further information: 

If interested, please contact Dr. Süleyman Er (s [368] er [28] differ [368] nl) +31 (0) 40 3334 936.

Website

www.suleymaner.com