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. Empa is a research institution of the ETH Domain. The Empa Laboratory of Cellulose & Wood Materials invites applications for a PhD student position on bio-based materials for advanced wound healing
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encourages excellence, individual recognition and responsibility. The Faculty of Biology and Medicine (FBM ) of the University of Lausanne is inviting applications for a position of: Tenure Track Assistant
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programming, modelling, and data analysis skills. Experience with formulating and solving mathematical optimization problems is an asset. Proficiency in English is required; good comprehension and oral skills
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elliptic versions), using shuffle algebra tools and with an eye toward applications to combinatorics, integrable systems, algebraic geometry and categorification. They should also actively contribute
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problems. The succesful candidate will work on the application and extension of data-driven nonlinear model reduction based on the recent theory of spectral submanifolds (SSMS). SSM-reduction is a dynamical
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Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Curious? So are we. We look forward to receiving your online application with the following
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environment that allows everyone to grow and flourish. Curious? So are we. We look forward to receiving your online application with the following documents: CV including publication record Brief statement
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forward to receiving your online application with the following documents: A motivation letter Your CV Job references and diplomas Please note that we exclusively accept applications submitted through our
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collaboration with companies or using company-level data. We are looking for candidates with expertise in either Smart Manufacturing and/or Operational Excellence: Smart Manufacturing covers the application
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understanding of district heating and cooling, renewable energy integration, multi-energy systems, and energy conversion and storage technologies. You have strong skills in programming, modelling, and data