Positions

PhD

• Geometric machine learning in quantum chemistry: learning kinetic energy density functionals. If successful, this project will enable a breakthrough in the computational cost of quantum chemical calculations. A solid understanding of quantum mechanics and machine learning are required. We work on this exciting project together with the labs of Andreas Dreuw, Maurits Haverkort and Björn Malte Schäfer.

We offer a supportive environment, and MINT-minorities are welcome!

BSc, MSc theses

BSc and MSc students are full team members and have a working place in the lab. Science is infinite (hence we always have a long list of open questions) but unfortunately lab space isn’t. As a consequence, we can only offer a finite number of theses at any one time.

Without exception, our thesis topics are connected to our own research, and by extension to things unknown. The ideal topic has a down-to-earth part (less exciting, but with very high chance of success) and a freestyle part (more difficult and higher risk, but also more exciting and with more space for creative contributions).

Besides the advising PI, most undergraduate students are also mentored by a PhD student in day to day work.

We try and find a good match between person and topic: To let you build on your strengths and experience, but also let you learn new things and give you space to grow. To apply, write to Fred Hamprecht with a short description of your interests plus CV and transcripts. MINT minorities (including female students and first-generation students) are particularly welcome!