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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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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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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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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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, 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
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, biosciences or a related subject area sound knowledge in the fields of plant stress signaling experience in cloning and plant biology knowledge in the role of temperature signaling in biology experience in
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comprehensive knowledge of optical properties, properties of the electronic structure, and electro-optical properties of the innovative novel d-electron element containing group-three nitride compounds, e.g. [(Sc
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spoken English skills Following competences are desirable: Solid knowledge of solid-state (semiconductor) physics Good knowledge in surface science Experience in epitaxy and surface science methods Basic
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comparable degree in molecular biology, biochemistry, mathematics, bioinformatics or any related areas, awarded by October 2026; some experience with computational or biological research; knowledge in biology