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Field
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an adaptive, self-optimising manufacturing ecosystem where production lines can reconfigure in real-time, in collaboration with domain experts (e.g. operators, planners, designers) that are supported by digital
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Participate in teaching and supervision activities, in line with the candidate’s profile and interests The research activities will be hosted by the Parallel Computing and Optimisation Group (PCOG) at SnT
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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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laboratory-scale R&D on PCM-integrated heat exchangers, develop modelling tools (e.g., COMSOL, ANSYS, TRNSYS, MATLAB) for thermal and system-level optimisation, and support prototype fabrication
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problem-solving skills and working knowledge or ability to learn languages commonly used in engineering (MATLAB, python). PREFERRED: Experience teaching subjects relevant to the curriculum. This is a one
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and targeting specific applications. Job Duties The job duties will be the following: Optimise stacks and processes towards the production of new sensing devices beyond current state-of-the-art; Micro
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optimisation of system efficiency, scalability and operational performance. The position offers an excellent opportunity to engage in applied research at the intersection of waste management, system design and
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printing software, etc.; proficient in software engineering using C/C++, OpenCV, Matlab, Unity3D, etc.; knowledge of machining; Linux and development on Linux systems; and proven system integration and
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this appointment. Key attributes of the successful applicant include: Strong coding skills (e.g., MATLAB) Advanced statistical experience An excellent publication track record Experience of conducting research with
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of post transcriptional regulation in pattern formation of the developing vertebrate embryo. You will help to optimise a system for high throughput quantification of segmentation clock dynamics and will