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-conversion and is closely connected to the Munich Center for Quantum Science and Technology (MCQST), providing an interdisciplinary environment linking condensed-matter physics, quantum theory, and materials
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molecular level. To yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and
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tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in the form of graphs to analyze and predict food-effector systems. Key
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. Our work is interdisciplinary, international, and in-depth, but also practical. We offer the possibility to obtain broad and profound expertise, both theory and practice, in the field of agricultural
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, physics and chemistry. Our goal is to combine data-driven modelling, theory, and experiments to accelerate the discovery of materials for future technologies. You will join a multi-cultural and cross
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systems and basic knowledge in information theory ▪ Proficiency in at least one programming language (e.g. Python) ▪ Interest in AI‑based attack models and security research The following points
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systems. Experience with robotic hardware (e.g., robot arms) and proficiency in C++ and/or Python, ROS 2, and MATLAB. Solid background in control theory and/or machine learning is highly desirable
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their thesis work in the field of robotics; Strong programming skills in C++ and/or Python, as well as experience in implementing robot learning algorithms; A strong background in control theory, machine
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: You will study the effects of privacy-preserving machine learning on memorisation, fairness, interpretability and model uncertainty utilising techniques from information theory and quantitative
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candidates will have a background in computer science, statistics, mathematics, or related fields, as well as an interest in social science research methods and theories. The PhD positions will be part of a