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similar field; expertise in programming skills and statistical data analyses, including machine learning; affinity with environmental exposure modelling and high-performance computing; strong reporting and
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interest in environmental health and Exposome research; expertise in programming and quantitative data analysis, including machine learning in R/Python; affinity with bioinformatics; strong collaboration
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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variants of the sodium channel Nav1.1, which are associated with different forms of epileptic syndromes and migraine. The aim of the project is to use machine-learning assisted molecular dynamics simulations
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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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critical component analysis, and (iii) development of Automation of ML model and data selection. The applicants should have knowledge of machine learning and optical networks and willing to engage in testbed
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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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below, this should include: Your CV A motivation letter An academic transcript Your contact details An academic referee For more information, contact Prof. Michel van Putten (CNPH) or Dr. Maryam Amir
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described in the project overview. Owing to the current composition of the project team, there will be a mild preference for candidates opting for project 2 on “Models and machine learning”. An explanation