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), robustness and flexibility. We cover the whole data processing chain including image capture, computational imaging and classification with classical and AI methods. You will develop concepts, specifications
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questions in the field of mathematical automation in numerical analysis or approximation theory. o You implement the developed methods in Lean. o You become an active team member of the "Mathematical Data
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the forefront of numerical analysis for Partial Differential Equations, enriched with data-driven methodologies -- a powerful combination that’s redefining what’s possible in computational science, and is playing
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Science). Experience in scientific writing and research methods (copy of the Master's thesis, as well as list of publications and presentations, if applicable). High scientific qualification in numerical methods and
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Science). Experience in scientific writing and research methods (copy of the Master's thesis, as well as list of publications and presentations, if applicable). High scientific qualification in numerical methods and
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of the most difficult problems in computational science. The ideal candidate will have prior exposure to modern developments in at least one of these fields: functional analysis, numerical analysis
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variety of methods. Generating recombinant bacteria. Testing bacteria for the production of new natural products. Data analysis. Contributing to collaborate projects. Contributing to writing publications
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What to expect: The minimum gross annual salary on a full-time basis (38,5 h / week) according to the collective agreement is EUR 59.934,--. The actual salary will be determined individually, based on your qualifications and experience. In addition, we offer company benefits, flexible working...
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better understand our world. Does this sound like you? Then join our accomplished team! Your personal sphere of influence: The Computational and Soft Matter Physics Group at the Faculty of Physics invites
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to invite applications for a University Assistant (Praedoc / PhD Candidate) to join the research team led by Univ. Prof. Olga Mula. Our group’s work sits at the forefront of numerical analysis for Partial