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of robotics, electromobility and autonomous driving. We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and
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science. A wide range of quantum theoretical methods shall be employed. A solid background in quantum mechanics and programming skills are prerequisite for this position, as is the readiness to learn and to
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., machine learning, stochastic dynamic programming, simulation). Affinity with (food) supply chain management is preferred. To collaborate with and to co-supervise MSc thesis students and internship students
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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theoretical methods shall be employed. A solid background in quantum mechanics and programming skills are prerequisite for this position, as is the readiness to learn and to apply new methods. For an initial
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., health and climate/environmental data) and could include a range of data science methods, such as utilising geographical information systems (GIS), statistical analysis, machine learning, deep learning
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ID: PMP_TRR408_C5 Investigators: Prof. Dr. Allister Loder, TUM Professorship of Mobility Policy, and co-supervised by at least one leading international researcher Requirements
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aims: Develop end-to-end protocols for screening selected foods and nutraceuticals. Create advanced strategies for data integration using tailored algorithms and machine learning approaches. Demonstrate
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
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approaches (e.g. SPG) as well as the use of machine learning, advanced computing, statistical modelling to explore the stochastic response to complex scenarios. This project offers the opportunity to undertake