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deformation. Responsibilities Develop scientific machine learning methods in close collaboration with team members specializing in experimental techniques and materials science. Utilize unique experimental data
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DTU Management’s Management Science division. The project is led by Professors Stefan Ropke and Richard Lusby and involves international collaboration with leading researchers in machine learning and
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be awarded a master’s degree in any of the following disciplines: Chemical Engineering, Physics, Mechanical Engineering, Energy Engineering, Environmental Engineering, Computer Science, or other
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Engineering, materials science or related subject. English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website
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seek a proactive and enthusiastic individual with a first-class or upper second-class honours degree (or equivalent) in: Engineering (Mechanical, Materials, or related disciplines) Physics, Chemistry, or
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interfacing of the sensors. The PhD candidate will work at the Integrated Devices and Systems (IDS) group within the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University
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degrees in either the natural sciences (chemistry, physics, mathematical/computational biology) or in the formal sciences (statistics, computer science, mathematics), but must have a serious interest in
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of the Open Competition Domain Science-M programme (Twenty-one innovative research projects awarded through Open Competition Domain Science-M programme | NWO - https://www.nwo.nl/en/news/twenty-one-innovative
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, engineers and PhD candidates. The PhD candidate is expected to develop an advanced engineering noise prediction model for efficient computation of sound propagation in a range-dependent atmosphere where