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-level modelling of environmental exposures and health risks; Interpretable and uncertainty-aware machine learning for heterogeneous health data. This position offers the opportunity to work in a
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an advantage: plasma surface functionalization; electrode/electrolyte interfaces; battery degradation modelling; microstructure-resolved modelling; tomography or image-based electrode modelling; machine learning
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-scale nature, complexity, and heterogeneity of 6G networks, we use tools such as artificial intelligence/machine learning, quantum computing, graph theory, graph-signal processing, and convex/non-convex
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Industrial Engineering and Operations Research. Good technical knowledge of probabilistic models and machine learning Basic knowledge of quality management in the manufacturing industry (e.g., SPC, FMEA, HACCP
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health technologies, we work closely with clinicians, engineers, computer scientists and policymakers to support the responsible development and implementation of innovative healthcare solutions. We
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to work on the design of novel nucleic acid delivery technologies for ex vivo/in vivo CAR-T cell engineering. The postdoc will assist in the development, optimization, characterization and (ex vivo/in vivo
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areas such as project management, reporting, and professional research and innovation practice, in the context of the 3 ECTS course “Soft Skills for Applied Computer Scientists”; You contribute, where
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machine-verifiable proof (or certificate) of correctness. However, a major limitation of current techniques is that correctness is not proven relative to the human-understandable specification
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of programming and implementing both back- and front-end solutions, using (open source) APIs. Evidence of being able to acquire additional funding (projects/scholarships) is a strong plus. You are willing to work
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addresses themes such as sustainability transitions, environmental governance in social ecological systems, human-nature relations, citizen science, biodiversity and biosphere integrity, social learning