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scheduling to help make offshore wind farms a reality. Job description This post-doctoral position focuses on developing fundamental algorithmic advances for dynamic planning and scheduling in multi-objective
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the next era of the quantum revolution. Together, we research, develop, and design innovative quantum algorithms that push the boundaries of what is possible- especially in the natural sciences such as
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radar prototype for remote vital sign sensing. The RA will help our team in the various project tasks that involve implementing different parts of the radar, including but not limited to: modifying
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sensors systems and UAVs at different scales. In particular, we will combine borehole and surface GPR as well as small-scale EMI measurements with root and shoot observations in controlled experiments
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responsibilities include: Development of a flood classification framework for flood type prediction Comparison of different ML algorithms in a sensitivity study Communication with stakeholders Development of open
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computers”(under the UKRI Guarantee scheme). Topics include: Quantum many-body dynamics Quantum algorithms Quantum-enhanced numerical methods Quantum machine learning Tensor Networks Topological order The
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. Design and implement multimodal unlearning techniques to address bias and privacy concerns. Evaluate the generalisability of multimodal learning across different socio-contexts. Validate the proposed
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laboratory facilities. More specifically TEC-EF responsibilities encompass at subsystem and instrument level: Payloads with RF interface exploiting different technologies (e.g., analogue, digital, optical
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simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state-of-the-art simulation algorithms to circumvent
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) region, and on synergetic approaches that can extract optimised information from different investigative methodologies. Research will be in liaison with the objectives of the Dust-DN project. Position