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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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at leading conferences and in leading journals tasks in teaching and academic self-administration Requirements: excellent university degree in mathematics, computer science, or physics extensive prior
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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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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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Mobility (IAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly automated, networked mobility, featuring
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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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) and the German Academic Exchange Service (DAAD) since 2007. Under this CAS-DAAD joint programme up and coming young Chinese scientists from the University of Chinese Academy of Sciences (UCAS) and CAS
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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