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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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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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Software Defined Vehicles (SDV). The primary objective is to expand, mature, and industrialize a novel European RISC-V automotive ecosystem that enables next-generation high-performance European automotive
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CO2 capture from the atmosphere. Your objectives will include to: Develop new optimization and/or machine-learning based reconstruction and segmentation algorithms to improve image quality in time
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(max 1000 words). Relevant research achievements, methodological approach and research objectives in the framework of the Postdoc are recommended elements to be included. Contact of at least two