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with privacy by developing techniques that optimize both aspects. The candidate will perform the work together with a team of postdoctoral researchers who are experts on the field and other PhD student
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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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to develop and optimize experimental pipelines.Gain experience in both wet-lab and computational techniques to tackle some of the most important questions in microbiome science.Independently drive research
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to scale up and demonstrate sustainable processes for industrial (bio)manufacturing of pharmaceuticals by integrating environmentally friendly technologies and processes. However, given the complexity
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, and data scientists to develop and optimize experimental pipelines. Gain experience in both wet-lab and computational techniques to tackle some of the most important questions in microbiome science
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for wind turbines, with the ultimate objective of including structural health information in windfarm asset management to optimise structural lifetime consumption while guaranteeing optimal power production
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of CRC-associated mucin mRNA isoform signatures in predicting the optimal therapy. These research objectives will be approached using established high-throughput next-generation sequencing, molecular
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. The central aim is to design intelligent systems that dynamically adapt the environment to support optimal learning conditions, based on real-time neurophysiological feedback. Key principles guiding
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for a m/f/x PhD Student – Subject: Quality Issues in cell and gene therapies YOUR JOB Development and validation of quality control assays for ATMP characterisation Design, optimization and validation
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for focal epilepsy with ultrasound neurorecording, modulation, and deep reinforcement learning (DRL) closed-loop control. The technology will be developed through detailed computer simulations and preclinical