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. Experience in Atmospheric Water Harvesting, metal organic framework modelling and machine learning is highly desirable. An ideal candidate will have a solid background in physics and chemistry, particularly in
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. Applicant(s) should possess a Ph.D. degree, or equivalent, in biological or computer sciences or a related discipline. Experience in any of the following fields, including 1) biological image acquisition
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execution. This work involves creating frameworks for adaptive decision-making, using techniques from operations research and machine learning. This particular thematic area will be supervised by Associate
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machine learning. This particular thematic area will be supervised by Associate Professor Agni Orfanoudaki. You will be responsible for planning and managing your own research programme within
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). • Advanced quantitative analyses (machine learning, computer vision, multilevel statistics). • Creation and use of Python code for advanced analyses. • Management and monitoring of complex transgenic lines
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discipline expertise and computational areas, such as data science, artificial intelligence (AI), machine learning (ML), generative AI and other technology innovations. Key details of this position include
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development in large multi-site cohorts; and 3) developing advanced machine learning models of task-related brain function. The fellow will have the opportunity to contribute to these projects and will also be
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evidenced by a strong publication record in one or more of the following areas: random matrix theory, stochastic processes, neural networks, machine learning, and sparse data structures • Proficiency in
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, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative modelling (diffusion/transformers), multimodal representation learning, and experience in
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: Support the development of AI and machine learning algorithms for autonomous navigation. Assist in building digital twin models to monitor drone health and mission performance. Contribute to IoT integration