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for latent variables and their connections to modern machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates
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with health/real grid data or machine learning is beneficial but not mandatory. We Offer One year funded of 3-year PhD position (SIF Doctoral Student Grant). Access to high-performance computing
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school, and taking part in the supervision of students. A limited amount of teaching may be included (max 20%). Requirements PhD degree in machine learning, scientific computing, statistics, physics or a
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limited to: “Data Analytics for Social Research”, “Applied Analytical Statistics for Social Scientists”, “Machine Learning with Social Data” and “Social Networks Analysis”. The appointee should be prepared
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Excellent English proficiency, spoken and written IT user skills (computer literacy) Ability to work in a team Desirable: Good knowledge and solid understanding of language learning and teaching research with
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration, experimental testing, or hardware-in-the-loop
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decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch energy
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Join us in developing machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic-inorganic materials for sustainable energy devices. Your work
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including the emergence of the United States as a global economic, political, military, and cultural power. Open Learning Faculty Members are teaching staff in the Open Learning division of TRU, and the role