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, such as R, Python, or Machine Learning, to identify patterns in biological factors, disease and mortality; co-supervising and mentoring PhD candidates, MSc and BSc students; collaborating with national and
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partners to reduce CO2 emissions in steel production using machine learning. You can find more information here . You will work on a theoretical and an applied project on data-enhanced physical reduced order
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light/heating modules, and selection and sorting routines. Guided by machine learning, we will perform directed evolution experiments where we optimize the synthetic genome that encodes for a biological
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integrates OLA production of liposomes, trap arrays, local light/heating modules, and selection and sorting routines. Guided by machine learning, we will perform directed evolution experiments where we
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, and you are expected to develop showcases for this new platform, and develop ideas to implement in a business. For this you will learn and exchange ideas within the Biotech Booster community
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applications* in close collaboration with other discipline experts (software, microelectronics and applications engineers). * except for RF payloads. ** including artificial intelligence and machine learning
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records from satellite data, and/or improved methods of uncertainty characterisation, including the use of artificial intelligence and machine learning to improve or analyse satellite climate data records
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in combination with other machine learning techniques, to create predictive models. You will engage in an interactive feedback loop with domain experts to analyze discovered models and remove any
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develop a simplified model focusing on the leader stage. You will: Analyze experimental data and microscopic simulations Identify relevant physical features and parameters Apply machine learning techniques