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Kibsgaard and Professor Stig Helveg, DTU Physics. We favour candidates with a degree in physics, chemistry or materials science. The candidate should preferably have experience in electron microscopy
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qualifications As our new colleague in our research team your job will be to develop novel computational frameworks for machine learning. In particular, you will push the boundaries of Scalability, drawing upon
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for the efficient formation of high-value compounds. Advanced NMR methods and computational data analysis will be compounded to devise novel reactions towards pharmaceutical precursors, polymer building blocks and
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topic of this project and who have academic training in one or more of the following areas: A master’s degree in chemistry, chemical engineering, materials science, physics, or a related discipline
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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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level equivalent to a two-year master's degree. The ideal candidate will have a background in photonics and condensed-matter physics. You should have a passion for theoretical and computational physics, a
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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution