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-informed / simulation-aware modeling Efficient algorithms for design-space exploration (e.g., surrogate modeling, Bayesian optimization, differentiable programming) Hybrid approaches combining data-driven
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use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography. Industry
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: advising them on experimental design, sample preparation and data interpretation You contribute to the continuous improvement of workflows, quality standards and documentation within the core facility You
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and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team
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production of bacteria via mass spectrometry Statistical analysis of the resulting data The successful candidate will have the opportunity to work towards a PhD Required qualifications: University degree (M.Sc
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, agricultural sciences with a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a
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that is relevant for applicants is some time before the deadline for the nomination at the ministry. Further information can be obtained directly at the respective higher education institution.