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intrinsic noise. These skills will be gained in part through scientific research that applies techniques from statistical and condensed matter physics to understand how phenomena in biological systems – key
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PhD Working Language German English Required Degree Master Areas of study Chemistry, Theoretical Chemistry, Nanomaterials, Physics, Theoretical Physics, Artificial Intelligence, Computer
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on the selection process, frequently asked questions, etc please see https://www.mhh.de/hbrs
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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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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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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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must submit a complete application for the programme via the DAAD Portal. Please be aware of the DAAD’s application deadline as well as the Universidad Nacional San Juan deadline. The application process
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(or equivalent) in computer science, applied mathematics, physics, engineering, biology, plant and agrosphere sciences, or another relevant natural science; Strong background in Structural Bioinformatics and
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applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf file to vpajob at tu-dresden.de or to: TU Dresden, Chair of Traffic Process Automation
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data from the European XFEL facility at DESY. Project website: https://www.mpinat.mpg.de/628848/SM-Ultrafast-XRay-Diffraction Your profile Eligible candidates have strong skills in computational