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consumption while guaranteeing optimal power production. You will work on the cutting edge of both wind energy and machine learning, two of the fastest growing scientific disciplines, to develop graph-based
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farm layouts with high specific power density through the co-optimization of array design and control strategies. This project is a collaboration with CNR-INM Institute of Marine Engineering in Rome
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design, optimization, and triage Conduct in-depth SAR analysis, combining experimental and in silico data towards the design of therapeutics Apply and supervise structure- and ligand-based design
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engineering of the polymer/monomer purification steps. The main objectives are: To gain physicochemical insights in the interaction between polymers present in footwear and solvents/reagents To optimize
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model-based control tailored to systems like NudgeFlow. The framework decomposes the global control problem into smaller local problems for each zone or room, enabling independent optimization with local
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on the design, optimization, and implementation of integrated quantum sensor architectures. As part of a multidisciplinary team, you will explore innovative, energy-efficient approaches to precision sensing in a
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, repairability, reuse, and/or recycling. Numerical and/or experimental analysis and modelling of machine components with the aim of improving their durability, reliability, and optimizing performance, and energy
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nature, involving optimization, control, data science, and/or machine learning problems will be evaluated positively. An interest in issues related to the challenge of the transition will also be
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) mouse model to improve outcomes. A successful project will result in: A developed and optimized mouse EVLP platform. AI-based identification of 3 IRI and 3 rejection target genes expressed
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optimizing simulation tools such as CalPhad to support experimental findings. Conducting in-depth metallographic analysis and establishing correlations between mechanical properties and microstructural