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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
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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
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or explainable AI or safety). Experience in machine learning, causal inference, image processing, human-robot interaction, or large language models. Experience in analyzing multimodal data (e.g., text, sensor
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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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learning and their own role as a teacher, and thus be competent to teach preclinical pharmacology. Furthermore, the applicant must demonstrate well-documented expertise in supervision at first cycle
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clinical service, appointments of trust in trade union organizations, or similar circumstances. Doctoral degree should be within bioinformatics, machine-learning, computational biology, genomics, or a
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and documented background in machine learning, deep learning, data analysis and programming. Previous experience in research and knowledge in bioinformatics, biophysics, biochemistry, molecular biology