161 genetic-algorithm-computer positions at Technical University of Denmark in Denmark
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following qualifications: Programming skills and AI interest Data analytical skills and computer science focus Experiences with chemical analysis and pilot experiments is an advantage. Knowledge
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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
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. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by
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passionate about working in a team. You will lead the publication of results in high-impact scientific journals. You have a strong background in Physics, Geophysics, Materials Science, Computation, Engineering
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section Energy Technology and Computer Science, where you will have around 20 colleagues with a mix of research and industrial experience. We work with research, innovation, technology implementation, and
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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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. The ideal candidate will have a strong background in Physics, Geophysics, Materials Science, Computation, Engineering, or a related discipline. Documented research experience in one or more of the following
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general planning of the PhD study programme, please see DTU's rules for the PhD education . We offer DTU is a leading technical university globally recognized for the excellence of its research, education
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“Bioactives – Analysis and Application”. As part of this prestigious Alliance PhD program, you will collaborate closely with Queensland University in Australia and the University of Copenhagen in Denmark
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: Background in Data Science, Computer Science or related fields; Working experience in implementing AI models (not just loading pre-trained model). PyTorch framework is preferred; Experience with APIs