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) • The successful candidate will have the opportunity to work towards a PhD Required qualifications: • Completed university degree (M.Sc. or comparable) in biology or a related field • Solid knowledge of molecular
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) • The successful candidate will have the opportunity to work towards a PhD Required qualifications: • Completed university degree (M.Sc. or comparable) in biology or a related field • Solid knowledge of molecular
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. Qualifications: • Completed academic university degree (Master level) in mathematics, computer sciences, physics or a related discipline • Knowledge of programming, machine learning methods, mechanistic modelling
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modeling and computational workflows Knowledge about machine learning: statistics and deep learning Experience in data analysis, visualization and presentation Good programming skills in languages such as
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tolerance of these novel materials and to enable a knowledge-based assessment of their suitability for future nuclear systems. At the same time, the successful candidate will contribute to maintaining
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Description The International Max Planck Research School “Knowledge and Its Resources: Historical Reciprocities” (IMPRS-KIR) invites applications for 3 doctoral positions, to begin on September 1
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for a motivated PhD student with a good knowledge of algorithms and theoretical computer science. The successful candidate will take part in the research and teaching activities at the Chair of Algorithms
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Emmy Noether Research Group, Situated Care: Subjectivity, Knowledge, and Labor , is looking for highly qualified and motivated candidates for 3 Doctoral Students (m/f/d) for “an ethnographic study of
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, their achievements and productivity to the success of the whole institution. At the Faculty of Psychology, Institute of General Psychology, Biopsychology and Methods of Psychology, the Chair of Cognitive and Clinical
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, dissertation) Your profile University degree (Master’s or Diploma) in Materials Science, Physics, Materials Engineering, Nuclear Engineering, or a related field Solid knowledge of materials characterization