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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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livable cities. The project will be based in the AI Laboratory for Molecular Engineering (AIME) , led by Assistant Professor Rocío Mercado Oropeza, where researchers develop new machine learning (ML
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. The project will be based in the AI Laboratory for Molecular Engineering (AIME) , led by Assistant Professor Rocío Mercado Oropeza, where researchers develop new machine learning (ML) methods to tackle
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to investigate flow-induced forces in hydraulic turbines under varying operational conditions and how these forces affect the degradation and lifetime of the machines. About the position The position is based
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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of quantum chemical calculations (DFT) is advantageous. An interest in machine learning and AI-based methods, as well as programming skills in languages such as Python or MATLAB/Simulink, is beneficial
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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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in Python programming. Experience with machine learning methods, bioinformatics, and data science. Familiarity with generative AI tools for protein design and protein language models. Knowledge
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. -Machine learning code generation for autonomous translation of payload data semantics. -Dictionary learning and algorithms for translation between major data modeling languages. -Model-based System
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Sciences division. This multidisciplinary team utilises a combination of machine learning and mechanistic modelling to derive models and scientific insights from data, which both support and enhance drug