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guidelines for the efficient and automated design and optimization of small-scale turbomachinery. Required competences Strong background in model order reduction techniques, surrogate modeling
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Engineering, Mathematics, Computer Science, or a related quantitative field A strong mathematical background, including familiarity with optimization, probability, and the analysis of mathematical models A
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Participate in education and supervise student projects More specifically, within this project, you will: Develop and apply multi-objective topology optimization to identify and characterize the
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modelling satellite imagery and textual conflict data. Advances in vision-language representation learning will be explored to link the two sources of information through alignment pipelines, localisation
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regular faculty, and gradually progresses to the latter. Instructors are expected to be able to teach courses in English or French. Applicants should have a PhD in Mathematics or in Computer Science
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background, and an outstanding MSc degree in Engineering, Computer Science, Physics, Applied Mathematics, or a related field. You should be proficient in or willing to learn generative deep learning – in
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, the Lausanne center for ultrafast science, and the Quantum Science Center at EPFL Competitive salary, coverage of conference costs, a dynamic and inspiring scientific community. Informations The
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Physics, Optics, Micro-/Nanotechnology, Materials Science, Energy, or a related quantitative field A strong background in several of the following fields: theory of light-matter interaction, computer
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international universities. CANDIDATE PROFILE We are looking for a PhD candidate with a strong analytical background and an outstanding MSc degree in Computer Science, Robotics, Chemical/Process
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(20%). Profile An excellent MSc degree in Computer Science, Energy Science, Social Sciences, Environmental Sciences, Sustainability Science, Geography, or a closely related interdiscipline. A strong