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this role, you will contribute to the development of intelligent design workflows using CFD and data-driven methods to assess and optimise next-generation vessel designs. You will collaborate closely with
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highly motivated and technically proficient Research Fellow in Control and Embedded Systems Engineering to support the development and deployment of intelligent, safety-enhanced BMS technologies within a
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to the development of intelligent design workflows using CFD and data-driven methods to assess and optimise next-generation vessel designs. You will collaborate closely with project partners, applying state-of-the-art
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will primarily support the Head of School (Professor George Panoutsos, Chair in Computational Intelligence) and his research activities in the area of Machine Learning (ML) for Engineering, focusing
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. The successful candidate will become an active member of the Energy, Power and Intelligent Control (EPIC) research centre within the School of Electronics, Electrical Engineering and Computer Science (EEECS
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and values diversity acting as a role model and fostering an inclusive working culture Person Specification Essential: PhD (or near completion) in Computer Science, Data Science, AI, or a related field
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PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical Engineering, etc.), expertise in cutting-edge AI and
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PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical Engineering, etc.), expertise in cutting-edge AI and
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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Inorganic by-products and other Sustainable Precursors Benguerir, Morocco Faculty of Intelligent Computing and Data Science Hanoi City (VN) Faculty of Health Sciences Hanoi City (VN)