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digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design
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. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power electronics, and self
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the disparities. While foundation models offer great promise for creating more robust machine learning models for a wide array of tasks, it remains an open problem how to foresee their biases across that wide array
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: Strong background in control and optimization, preferably with experience in model predictive control (MPC). Solid skills in machine learning algorithms and data analysis. Experience with building
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Denmark as well as abroad. Your primary tasks will be to: Develop, test, analyse, simulate and predict the capture performance of new fishing gears. Produce high quality scientific and/or engineering papers
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are part of a sub-project on Algorithmic Sustainability in association with Professor Christina Lioma and her Machine Learning research team in the Department of Computer Science at the University
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simulations using, e.g., COMSOL, Lumerical, or other Maxwell solvers. Experience with machine learning algorithms is an advantage but not required. General qualifications Scientific production and research
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: www.jura.ku.dk . The Faculty actively supports efforts to learn Danish. Qualification requirements Employment as a Postdoc requires academic qualifications at PhD level. More information on careers at UCPH and the
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Post Doctoral Researcher in Human-Centered AI for Software Engineering, Department of Electrical ...
The Section for Software Engineering and Computing Systems, at the Department of Electrical and Computer Engineering (ECE), invites applicants for a two-year postdoctoral position within the area of
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You have academic qualifications at PhD level, for example within the areas of bioinformatics, machine learning or forensic odontology. We favour experience in computational data analysis, and the