PhD Candidate in Neural Network-based Modeling for Molecule and Material Design
2 weken geleden
We are seeking a highly motivated and skilled PhD candidate to join our research group in the field of computational chemistry and materials science. The successful candidate will be responsible for developing and applying state-of-the-art computational frameworks for studying electro-catalytic reactions and designing improved catalyst materials.
Key Responsibilities:- Develop a protocol for setting up, training, and applying neural network potential-based molecular dynamics simulations for catalyst screening.
- Develop and apply machine learning techniques, such as Collective Variable Free Sampling of Molecular Transition Paths, to obtain reaction free landscapes.
- Improve the integrated models for investigation of electron and proton transfer reactions at catalyst materials.
- Propose new catalyst materials, working conditions, and experiments to our collaborators.
- A Master's degree in science, preferably in chemistry, chemical engineering, physics, engineering physics, materials science, computational science, or artificial intelligence with a specialization in natural science.
- Experience with molecular simulation is preferred.
- Experience with machine learning is preferred.
- Good programming skills.
- Excellent communication skills in oral and written English.
- Strong initiative and good time management skills.
- A temporary contract for 38 hours per week for the duration of four years.
- A gross monthly salary ranging from €2, in the first year to €3, in the last year.
- A favourable tax agreement, the '30% ruling', may apply to non-Dutch applicants.
- A complete educational program for PhD students.
- Multiple courses on topics such as leadership for academic staff.
- Multiple courses on topics such as time management, handling stress, and an online learning platform with + different courses.
Please submit your application, including your CV, motivation letter, and contact information of at least two academic references, to [insert contact information].
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