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, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models, including hands-on implementation Strong understanding of machine learning models and their development Strong analytical
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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materials using various surface analytical techniques (e.g., XPS, SEM, etc.). Evaluate the performance of the functionalized textiles and assess their suitability for the targeted applications. Collaborate
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for specific textile functionalities (antibiofilm, repellence, adhesion). Characterize the deposited materials using various surface analytical techniques (e.g., XPS, SEM, etc.). Evaluate the performance
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graphs and related structures, limit theorems, stochastic calculus and applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department
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training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems Experience 0–3 years of postdoctoral experience
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machine learning technologies in order to provide evidence-based decision support tools in near real time across a variety of thematic domains: disaster risk reduction, sustainable agri-food systems