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data analysis and quantitative evaluation of imaging or histological data Fluent in English and strong communication skills in an international team environment Benefits Opportunity to integrate into a
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a small multicultural research group with PhD students and postdocs of different nationalities. Thus, the group’s communication is in English. We aim to analyze the metabolic homeostasis
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Max Planck Institute for the Structure and Dynamics of Matter • | Hamburg, Hamburg | Germany | 22 days ago
(DESY), Universität Hamburg (UHH), and the European XFEL (EuXFEL). Teaching language English Languages English Programme duration 6 semesters Beginning Only for doctoral programmes: any time Application
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. Programme duration 6 semesters Beginning Winter semester Application deadline 1 March Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme
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Research Foundation (DFG) is funding the project. In the course of the application procedure, which takes place both at the GRK 2767 of the TU Dresden and at the HZDR, the documents received by the HZDR will
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Requirements: excellent university degree (master or comparable) in computer engineering or electrical engineering a strong background in digital design, hardware description languages (e.g. Verilog, VHDL
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science. The proof must be submitted to the programme commission, which may propose further qualification measures in consultation with the supervisor. Language requirements Excellent English Submit
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machine learning (ML), artificial intelligence (AI) or related fields • Software skills in ML languages such as Python • Ability and enthusiasm to learn new technologies quickly • Ability to work highly
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Altersforschung: Fritz-Lipmann-Institut e.V. (fli) HKI (Hans Knoell Institute) Leibniz IPHT (Leibniz Institute for Photonic Technologies) Teaching language English Languages Courses are held in English. Programme
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, mechanical engineering, physics or similar basic programming skills in one or more languages (Python, C/C++, or others) experience in mechanical testing profound knowledge of machine learning methods (e.g