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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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Master’s degree (or equivalent) in a relevant discipline such as computer science, mathematics, physics, or data science. They should have strong analytical skills related to statistics, machine learning
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. Requirements: university degree (master or diploma) in chemistry or physics and profound knowledge in computational and theoretical physics/chemistry A sound knowledge of simulation methods and actinide
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or equivalent) in the field of aerospace engineering, physics or similar solid knowledge of Physical and Analytical Chemistry (phase changes), Computer Science and Informatics (Numerical Analysis; simulation
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, conversely, to use clinical observations to develop new research ideas. The aim is to deliver medically relevant benefits to patients and the general population. The BIH PhD Program contributes to the overall
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the Federal Ministry of Research, Technology, and Space (BMFTR). It includes an interdisciplinary training program based on the concept of a graduate school. In the 2nd year there is the option of increasing
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candidate will be embedded in the DFG Research Program RTG3120 on Biomolecular Condensates ( https://dresdencondensates.org ). Each PhD project is part of an interdisciplinary framework that includes shared
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on the selection process, frequently asked questions, etc please see https://www.mhh.de/hbrs
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to you on the website: https://tu-dresden.de/karriere/datenschutzhinweis . This project has received funding from the European Union’s Horizon Europe research and innovation program under the MARIE
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to 5 potential projects from either program. Contact For further information about the program please visit: imprs-gs.uni-goettingen.de or https://www.uni-goettingen.de/de/621713.html The Max Planck