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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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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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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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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
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in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here. Are you interested in learning more? Read about