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Field
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COG-MHEAR is a world-leading cross-disciplinary research programme funded under the EPSRC Transformative Healthcare Technologies 2050 Call. The programme aims to develop truly personalized
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and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis
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John Williamson, and Dr Sebastian Stein. The job requires the proven ability to develop novel theory and build and evaluate working interactive prototypes involving complex computational models
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at the interface between computational design and in vitro experiments, the team has developed multiple in silico and in vitro methods to design novel antibodies and nanobodies as therapeutic agents and research
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developing new methods and techniques that will improve standard ML algorithms so as to achieve good performance outside their training distribution, by treating high-dimensional problems as an explicit
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lab. Key Responsibilities Main Duties: Develop teleoperation interface with a robotic manipulator to facilitate human-robot interaction in remote handling. Design robot impedance control algorithms
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computational algorithms of different datasets, and implement novel algorithms within the framework of existing code, providing documentation and user support. You will be responsible for leading statistical
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, algorithm development, AI, and machine learning, supporting fusion energy engineering through advanced simulation and workflow optimisation. You will work on impactful, collaborative projects across academia
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. The scope of the research will encompass aspects such as network monitoring, routing algorithms and network-hardware benchmarking. About you You should possess a MSc/MEng in Engineering, Computer
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leverage low-precision accelerators for scientific computing by using a number of tricks, known as "mixed-precision" algorithms. Developing such algorithms is far from trivial. We can look at computational