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Field
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group members. • Participate in the development of theories, collaborate with the top experimentalists, and perform numerical simulations (matlab/python/mathematica). Key Requirements: • Hold a Master's
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pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with colleagues from various application areas (cross-disciplinary) publication and presentation of your scientific results
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programming skills (e.g., Fortran, Python; familiarity with Linux/HPC environments). strong motivation to work in an interdisciplinary environment bridging molecular theory, nanoscience, and light–matter
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learning pipeline in Python (using e.g. PyTorch) - validation of your results in collaboration with colleagues from various application areas (cross-disciplinary) - publication and presentation of your
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pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with colleagues from various application areas (cross-disciplinary) publication and presentation of your scientific results
-
software as python packages Report findings and methods in conference and journal papers Your profile Masters, Diploma or equivalent degree in IT/computer science/statistics/applied mathematics/data science
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communication skills in English.-A collaborative and curious mindset.-Experience with biological or solid-state nanopores, DNA nanotechnology, single-molecule microscopy, or programming/data analysis (e.g., Python
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in R and/or Python Excellent communication skills and fluency in English (oral and written) Independent, proactive, rigorous, results-driven, solution-oriented, and a critical thinker Collaborative
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mechanics, or advanced material behavior. Programming skills (e.g., Python, MATLAB, Julia,..), ideally with experience in numerical methods or scientific computing. Familiarity with machine learning
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software as python packages Reporting findings and methods in conference and journal papers Your profile Masters, Diploma or equivalent degree in IT/computer science/statistics/applied mathematics/data