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medicine, with a primary focus on optimizing clinical trial design. The partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry
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, fabrication, and testing of functional thin film coatings. Key responsibilities include: Developing and optimizing advanced surfaces using state-of-the-art thin film technologies Characterizing prepared thin
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into the same devices. The research project is part of a larger consortium, gathering world-class researchers in remote sensing with expertise ranging from estimation and optimization theory to hardware design
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theoretical basis for modelling functional biodiversity, based on eco-evolutionary optimality (EEO) theory. The PDRA will be explicitly responsible for statistical analysis of plant trait data and the
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an integrated model of plant carbon allocation, based on eco-evolutionary optimality theory. The PDRA will be explicitly responsible for designing an approach to evaluate and model carbon allocation to non
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Python, using GPUs), with the precise balance determined by the candidate’s background and interests. The mathematical tools involved will include matrix analysis, optimization, backward error analysis
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. AI-driven technology to achieve optimal performance Integrate developed PA techniques into practical active antenna array demonstrators. Conduct experiment with existing USRP platform for wireless end
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. The research focus of our group is on parallel computing, supercomputing, and performance tuning and optimization of advanced applications. Our team currently consists of 10 scientific and 3 administrative
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). The ideal candidate will have: A PhD in environmental science or closely related discipline by the start date of the appointment Broad understanding of eco-evolutionary optimality concepts and modelling
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forefront of hydrogen technologies and their integration to the energy systems. The successful candidate will hold a PhD degree (or close to completion) in Mechanical/Electrical Engineering or related subject