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Field
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of a robotic and autonomous laboratory for accelerated materials discovery and battery research. The role will focus on assisting with the integration of automated materials synthesis, sample handling
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molecular robots with Self-X (self-repair, self-adaptation, self-termination, etc.) capabilities. As part of this DBTL cycle linking AI-based molecular design with automated experiments, the successful
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ideas from machine learning, econometric choice modelling and mathematical psychology. You will investigate how large language models, optimisation algorithms and automated model search methods can
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powder bed fusion system, focusing on automation and control aspects of this novel machine and the implementation of an in-process monitoring system. This role is central to our efforts to push the
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/ technical hands-on experience in building energy/power system control & optimisation. Familiarity with building energy/power system control & optimisation, building automation and control (BAC) system
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molecular drivers. Develop AI agents and LLM-based computational workflows for biomedical research, including automated dataset discovery, quality assessment, multi-omics analysis, biological interpretation
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research for project titled: “Automated Loose Baggage Handling System for Narrow-Body Aircraft (NYALA)”. Key Responsibilities: Work on exciting robotics design Simulation in physics-based-framework such as
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. The RF is also expected to contribute actively to other research activities within EOS. Key Responsibilities: Develop and implement an automated post-processing workflow for GNSS data analysis using
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transformation (GenAI, decision intelligence, process automation) Digital platforms & ecosystems, venture building, scaling AI and Data-driven operations, service transformation, responsible innovation
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of the Pension Scheme is available. The post is based in London at LSHTM. Applications should be made on-line via our jobs website . Online applications will be accepted by the automated system until 10pm