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fellow devotes most of their time to research. There is the possibility of teaching up to 20%. Requirements Requirements PhD degree in in machine learning, automatic control, system identification, signal
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, regression models or machine learning. Applicants from clinical hepatology with experience in the above fields can also be interesting. You will work in an interdisciplinary and international research
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integration of AI-based vision and active machine learning to optimize the efficiency of the process. Writing publications and present research results from the project on conferences. Collaboration with
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HCI and cybersecurity, to cancer research tools and methods for numerical analysis and machine learning. The research work takes place in a multidisciplinary team with a focus on image processing with
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. Teaching may also be included, but up to no more than 20% of working hours. The position includes the opportunity for three weeks of training in higher education teaching and learning. The purpose
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methods to rigorously assess the safety and effectiveness of medications in real-world patient populations. Defining individualized treatment strategies: Leveraging traditional and causal machine learning
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groups working towards a common goal. For this postdoc project, we seek a dynamic and motivated candidate with an interest in computational electromagnetism, inverse design, and/or machine learning in
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measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning techniques and generative AI. A strong background in software engineering as well as some
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statistics, unsupervised machine learning, optimisation, model predictive control. Experience in financial mathematics. Having high integrity, be process-oriented and able to work independently. Being able
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scientific curiosity Mastery of data visualization and scientific communication Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently