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with the microfluidic live-cell imaging platform and provide Python/Jupyter-based tools for collaborators, contributing to FAIR datasets and open-source software (Co-)Supervise student projects at BSc
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sampling strategies in Python and C++ Apply your framework to analyse real biological datasets to demonstrate robustness, interpretability, and practical impact Contribute to open-source software tools
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the application which project you are specifically interested in. Further details on the projects can be found here: https://www.fz-juelich.de/en/jcns/careers/fellowships/tasso-springer-fellowship-program Your
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mathematics Proficient in programming (Python and C++) Fluent in written and spoken English; knowledge of German is an asset Strong analytical and independent working style Excellent teamwork and communication
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science, physics, or a related field Basic knowledge of perovskite PV or semiconductor physics Initial experience in with an object-oriented programming language (e.g., Python, Matlab) Analytical and
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of X-ray methods Knowledge of X-ray optics Knowledge of synchrotron science Knowledge of catalysis and energy storage Experience with programming languages (ideally Python), SPS controls system Fluent in
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Proven laboratory experience and programming proficiency (Python or a comparable language) Ability to work independently and self-reliantly, while maintaining strong teamwork and collaboration skills
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Independent way of working Basic programming skills (Python) advantageous Very good knowledge of German or English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ), ideally
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, programming (Python) and advanced data analysis methods Excellent oral and written communication skills in English Ability to communicate results clearly in presentations and in writing Self-motivated and well
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the consortium Your Profile: Excellent Master and subsequent PhD in computer science, engineering, biophysics, applied mathematics, computational biology or a related field Proven programming expertise in Python