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techniques (XAS, PES). Deriving a set of descriptors to generate new Ce-compounds with tailored functionalities from high-throughput/machine learning methods Requirements MSc graduate in theoretical physics
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for learning, reasoning, and acting based on data. An overarching goal is for both humans and machines to better understand the complexity of the real world. Probabilistic models form a central part of our
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using machine-learning techniques. The work involves colaboration with industry. Requirements For employment as postdoc, the applicant should have a PhD exam or an international equivalent within
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national registers and cutting-edge methodologies in epidemiology and artificial intelligence, including group-based trajectory modelling, causal mediation analysis, and machine learning based risk
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the equivalent knowledge. If special circumstances apply, this training for teachers in higher education may be completed during the first year of employment. Documented ability to teach in Swedish or English is a
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that apply one or more of data mining/analytics/science, artificial intelligence, expert systems, machine learning and natural language processing. The position involves up to 50% teaching. Included within
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experience of methods for investigating how different factors affect learning and teaching in mathematics. Applicants are mainly those who have completed a doctoral degree no more than five years before
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completed during the first two years of employment. Documented ability to teach in Swedish or English is a requirement unless special reasons prevail. The candidate is expected to be able to teach Swedish
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for teachers in higher education may be completed during the first year of employment. Documented ability to teach in Swedish or English is a requirement unless special reasons prevail. Personal capabilities
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. If you have specific questions regarding a course, please contact Hans Rosth. 1RT705/1RT003 Advanced Probabilistic Machine Learning, period 1 1RT490 Automatic Control I, period 1 and 2 (given in Swedish