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considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an advantage. Knowledge of or a passion for sustainable computing
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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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Sustainability in association with Professor Christina Lioma and her Machine Learning research team in the Department of Computer Science at the University of Copenhagen. The sub-package focuses
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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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(KCL, London, UK) but will also have the opportunity to travel and work at the Centre for AI and Machine Learning (ECU, Perth, AU) and the School of Psychiatry and Clinical Neuroscience (UWA, Perth, AU
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reactions. We welcome applicants from diverse backgrounds, including computational chemistry, bioinformatics, systems biology, and machine learning. The project offers a unique opportunity to collaborate
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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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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