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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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will use machine learning methods to develop affinity ligands. These methods have been transformative for protein design, allowing generation of novel proteins which can suit a precise need. In this 4
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with machine learning and generative AI algorithms, with working knowledge of deep learning frameworks such as PyTorch or TensorFlow is considered a strong advantage. • Extensive experience in multi
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fluids, flow-induced pattern formation in both simple and complex flows (e.g. flow instabilities, product defects), multiscale analysis, and the application of machine learning techniques. About the
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performance in organic electronic and electrochemical devices. Multiscale simulation and integration of machine learning: Use molecular dynamics, quantum mechanical and continuum models, in combination with
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on how your research can be further developed into innovations. You are interested in driving the integration of methods in artificial intelligence (AI) and machine learning (ML) to improve and optimize
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artificial intelligence (AI)/Machine Learning (ML) with a focus on life science, or alternatively, life science with a focus on AI/ML (or equivalent). You will work closely with researchers, engineers, and
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changes and established markers for Alzheimer's disease. The project may also include machine learning methods to estimate individuals' biological age. The project is based on existing data from a prominent
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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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managing large amounts of data by designing structured databases (PostgreSQL, MySQL). Machine learning methods such deep learning for analysis of proteomics data and classification of cancer profiles. Since