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at the interface of machine learning, statistics, and live-cell biology. The position is co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and the student will be equally
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overuse injuries. Wearable sensors to quantify of the impact and benefit of sleep on the recovery, performance and overall wellbeing of athletes. Using big data and machine learning methods to identify
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(supervised by Assoc. Prof. Ivana Konvalinka) and machine learning researchers (co-supervised by Prof. Lars Kai Hansen), you will be responsible for designing and running interactive multi-person (hyperscanning
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of students and can include, technique development, microscopy-spectroscopy, analysis/programming (including AI and machine learning) and materials-focused studies. We use innovative high-resolutionidentical
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of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation of the mathematical models of the patho-physiology and PK/PD
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with, cloud computing and virtualisation technologies Familiarity and hands-on experience with machine learning techniques desirable Desirable to have work experience (through internships or similar) in
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complementary and synergic methods at the intersection of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation
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Ingolstadt. The salary is prescribed by the framework of the collective agreement (TV-L), Level 13 (75%). The research group Reliable Machine Learning (headed by Prof. Felix Voigtlaender) is part of
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of everyday life. This project aims to change that by developing AI-driven methods to assess wellbeing through video-based sentiment analyses. As a PhD student, you will develop and refine machine learning
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, into the research groups of Prof. Oliver Buxton whose expertise is on turbulence, wind-energy flows, and turbulent cloud microphysics and Prof. Luca Magri whose expertise is in scientific machine learning