885 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" positions in Sweden
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within applied mathematics, materials science, physics and building science. Mathematical statistics is an important part of these four areas of research. Relevant applications include machine learning
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at SLU by visiting: https://www.slu.se/en/about-slu/work-at-slu/ Location: Uppsala Form of employment: Temporary employment 24 months, with the possibility of extension. Scope: 100% Start date: As agreed
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dynamics for shape change. A further aspect of the project is learning and calibrating these models from data using data-driven inference methods. Who we are looking for Required qualifications A doctoral
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, workplace learning, educational leadership, communication, school development, didactics and teaching. For more information about doctoral education in education at the University of Gothenburg: https
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. The position is a fixed-term appointment of four years, with the possibility to teach up to 20%, which extends the position up to five years. A starting salary of 34,550 SEK per month (valid from May 25
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are self-driven, eager to learn, and possess good analytical problem-solving skills Programming skills in Python or Matlab. You are expected to be somewhat accustomed to teaching, and to demonstrate
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. The department will provide support with language learning. Eligibility The applicant must meet the following qualification requirements: PhD or equivalent academic qualifications research expertise in
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-energy devices. Using state-of-the-art electronic-structure calculations and machine learning methods, you will model these effects and contribute to the design of improved semiconductors for solar cells
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the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
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, or quantum-inspired methods Experience with hybrid quantum–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience