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date is (expected to be) March 1st 2026 or as soon as possible thereafter. The project UP2MEN will train 15 doctoral students to face the complex challenge of predicting the impact of pollutants
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of electrodes and to map electrolyte chemical composition in micrometer resolution, allowing validation of the model predictions. Validation and evaluation of the RFBs with optimized hierarchical electrodes. Job
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machine learning techniques to develop local graph representation models, which will be aggregated globally to enhance their predictive power and translational relevance, all while maintaining strict data
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and operation of building HVAC systems, these technologies support both energy efficiency and flexible demand objectives. Model predictive control (MPC), which involves physics-based building energy
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inhibitor candidates with high predicted affinity and selectivity. These designs will then be experimentally validated through a combination of affinity binding assays, enzymatic activity measurements
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to develop new methods. The proteomics data will be used in combination with protein structure predictions and functional studies to understand the structure, function, and assemble of multimeric protein
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and predict cyber threats. You will work closely with international collaborators, including Eindhoven University of Technology (TU/e), and with industrial partners, providing realistic case studies
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modifications using high-resolution mass spectrometry and AI-based de novo peptide sequencing. Develop and apply machine learning models to predict protease activity and substrate specificity, integrating protein
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safely share data while maximizing utility–privacy trade-offs. Decision-support pipeline: fuse predictive and prescriptive analytics, so that forecast providers and aggregators can maximize the value
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. Responsibilities and qualifications You will contribute to the development of a computational framework designed to predict the degradation mechanisms of organic electrolytes. The framework will rely