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on diffusion and/or flow matching, that can rapidly adapt pre-trained models to new tasks and select optimally informative simulations at each stage in a design process. Collaborate closely with PhD students
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are seeking candidates with a PhD in Mathematics who can successfully conduct research in one or more of the following areas: Von Neumann algebras Analysis of quantum information Quantum Markov semigroups
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, scientific machine learning (ML), wind energy, and advanced optimization? Join our team to develop cutting-edge solutions for aerodynamic design optimization of wind energy systems in complex urban
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large spatio-temporal data sets and mechanistically modelling soil C processes. Together with the project team, which consists of a postdoc, a PhD student, a technician, the project PI, and several
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working with dr.Rudy Helmons (dredging, marine minerals) and dr. Austin Kana (design methodology, complex systems), where you will work on the development of an assessment framework to identify Best
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of the world’s most highly ranked (and most comprehensive) research, education and innovation communities devoted entirely to aerospace engineering. More than 200 science staff, around 270 PhD candidates and close
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capacity building through training materials and resources tailored to practitioner needs, and policy and practice innovation by integrating gender-sensitive perspectives into counter-radicalisation
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Helmons (dredging, marine minerals) and dr. Austin Kana (design methodology, complex systems), where you will work on the development of an assessment framework to identify Best Available Technology (BAT
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presentations in which you communicate your experimental results. Job requirements You would be our ideal candidate if you are/have: PhD in the field of (applied), physics, chemistry, (fibre)optics, material
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data. There will be regular meetings with the project’s soundboard group, consisting of (academic) experts in innovation and biotechnology, and you will report on the progress during those meetings