182 bayesian-object-detection positions at Technical University of Denmark in Denmark
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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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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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causing these variations in A and F stars. Using asteroseismology, we aim to detect and analyze near-core and surface magnetic fields. This involves comparing theoretical models with photometric
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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
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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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abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark . Application procedure Your complete online application must be submitted no later than 20 June 2025 (23