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Field
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data using multivariate statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft
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, both over the wireless interface and within the core network, will be driven by AI and machine-learning applications. This research will develop efficient communication strategies to support
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, such as pulse design or numerical optimization Background in data-driven or machine-learning approaches relevant to optimal control (e.g., model learning, reinforcement learning) What you will do Take
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Master's degree in computer science, computer engineering, or equivalent. Demonstrate proficiency in English (reading, writing, speaking). Show the ability to work independently and in a team, as
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equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our
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and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed
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lecturer who can complement our team with expertise in gender studies that is clearly oriented towards global studies and/or human rights, and who can teach, supervise and communicate with students in
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, appointments of trust in trade union organisations, military service or similar circumstances, as well as clinical practice or other forms of appointment/assignment relevant to the subject area. The doctoral
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to the forefront of quantum technology, and to build a Swedish quantum computer. Building a quantum computer requires a multi-disciplinary effort involving experimental and theoretical physicists, electrical and
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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