PhD Candidate in Generative AI for Inverse Design of Molecules and Materials

6 dagen geleden

Amsterdam, Noord-Holland, Nederland Universiteit van Amsterdam Voltijd
Job Description

We are seeking a highly motivated PhD candidate to join our research group in the Faculty of Science at the University of Amsterdam. The successful candidate will be part of a dynamic team working on the development and application of machine learning techniques for the design and simulation of molecules and materials.

Research Context

Designing new molecules and materials is a complex task that requires a deep understanding of the underlying chemistry and physics. In this project, we will develop and apply machine learning algorithms to predict the properties of molecules and materials, and to design new compounds with desired characteristics.

Key Responsibilities

  • Develop and apply machine learning models for the prediction of molecular properties
  • Design and simulate new molecules and materials using machine learning algorithms
  • Collaborate with experimental researchers to validate the predictions and designs
  • Contribute to the development of new machine learning techniques for molecular design and simulation

Requirements

  • Master's degree in a relevant field (e.g. chemistry, physics, computer science)
  • Experience with machine learning algorithms and programming languages (e.g. Python, R)
  • Good communication and teamwork skills
  • Interest in collaborating with experimental researchers

What We Offer

  • A temporary contract for 38 hours per week for the duration of 4 years
  • A gross monthly salary ranging from € 2.872 in the first year to €3.670 in the last year
  • 8% holiday allowance and 8.3% year-end allowance
  • Multiple fringe benefits, including 232 holiday hours per year and extra holidays between Christmas and 1 January

How to Apply

Applications should include a detailed CV, a 1-page letter of motivation, and contact details of two academic referees. We accept applications until and including November 1st, 2024.

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