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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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PhD project involves interdisciplinary research at the interface of computer science and mathematics and addresses a complex, coupled inverse problem with explicit uncertainty quantification. Research
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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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events are foreseen, applicants must be ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu-dresden.de/ing/maschinenwesen/postgraduales/promotion
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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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programs, the focus lies on individual research projects, complemented by program specific seminars as well as an individualized curriculum comprising lab and soft-skill courses, congresses, symposia and
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letter Application Application https://www.cens.lmu.de/en/support/career-at-cens/ https://www.cens.de Contact LMU München Center for NanoScience Address StreetGeschwister-Scholl-Platz
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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
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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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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