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experimental data, our ability to develop predictive models for the properties and long-term performance of reactor materials is also limited. This position is part of the MAGIC-RR project (Materials Ageing and
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their involvement in disease resistance under controlled conditions. Our model system is the bacterial pathogen Xanthomonas campestris pv. campestris (Xcc), which causes black rot disease in cabbage
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machine learning and neuromorphic hardware is to utilise material platforms that exhibit multiwell behaviour. One challenge here is to understand and control the dynamic response of the system for complex
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targets will then be investigated in obese animal models to dissect the underlying cellular mechanisms and test potential compounds that modulate microglial pathways, with the ultimate goal of developing
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complex socioeconomic, environmental, and molecular data in the context of multigenerational families. · (Co-)developing predictive models based on very large registry, medical, social/environmental
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of functional glycans in situ and development of advanced infection model systems to study the role of functional receptors. These models will include (genetically engineered) stem cell-derived 2D and 3D
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physics, microscopic modelling of different materials, and quantum information theory. Hybrid Super-Semiconducting Systems: You will contribute to pioneering research in hybrid super-semiconducting systems
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in Data Science, Artificial Intelligence, Computer Science, Cognitive Science, or any another relevant discipline. Have interest and experience with deep learning and image analysis. Have interest and
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gaining teaching experience; Ability to drive a car (driving license B). Additional Information Benefits We offer: a position (1 FTE) for 36 months. a working week of 36 - 40 hours and a gross monthly
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information science. Good knowledge of mathematical and theoretical physics methods of describing random processes and random fields. Experience with analytical and/or numerical methods for modelling qubit dynamics