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. You will draw on ideas from Bayesian optimization and Bayesian deep learning, generative modelling, high throughput screening, and combinatorial synthetic chemistry. Responsibilities and qualifications
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machine learning for transport simulation. A core innovation involves Bayesian metamodeling techniques to construct fast surrogate models of the simulation space, enabling efficient scenario analysis
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expression and developability. Propose and validate optimization tools for performing (Bayesian) design of experiments. System validation and iterative refinement based on empirical data. Test and refine
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of grading scale A 1-page research statement, including figures and references if deemed necessary (max 500 words). Research achievements (if any), methodological approaches and research objectives relevant
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a safer ocean together! The research of this project has civilian objectives. However, equipment restricted by export licenses and ITAR (International Traffic in Arms Regulations) is being used
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be an expert within object oriented fortran, CUDA, HIP, build systems such as CMAKE and Autotools, the Github CI/CD platform and schedulers such as SLURM and LSF10. The candidate should hold a MSc in
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objective is to surpass the current traditional thermodynamic and optimization approaches, which are constrained in design discovery capabilities and long-term TES performance evaluation. Through your
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products, and plastics & composite products. The objective of the post-doc is to facilitate and coordinate industry-university collaboration in the development and implementation of remanufacturing systems
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Technology (DTU Space), current focus areas cover large-scale structure of the universe, physics of compact objects, exoplanets, upper atmosphere physics and cosmo-climatology as well as development
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a collaborative research team and take responsibility for your individual research objectives. As this position is funded under the Marie Skłodowska-Curie Doctoral Network (MSCA DN) programme