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The post of Research Associate in Large Language Models for Process Automation will contribute to the development of an AI-powered framework for risk assessment and safety in continuous chemical
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will be expected to participate in co-supervision of project students, oversee smooth and safe operation of the lab. Applicants should hold a PhD in synthetic organic chemistry or physical organic
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, model predictive control, parallel computing using JAX and rapid online learning, is highly desirable, but candidates demonstrating an ability and willingness to become familiar with these topics and able
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on breath tests for respiratory infections in ventilated children, collaborating across disciplines and advancing your skills in bioinformatics and data science. You will lead and contribute to cutting-edge
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for maximum performance per watt. You will investigate how configurable and customizable processing technologies can be leveraged to create fundamentally more energy-efficient Computational Fluid Dynamics
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language and familiarity with machine learning libraries (e.g., PyTorch). Experience in structural Bioinformatics, including the ability to manipulate protein sequences and structural data. Collaborative
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learning for policy optimisation, all within a huge GPU-based simulation with thousands of robots learning synchronously in parallel. The best solutions will then be 3D-printed, and policies will be fine
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with thousands of robots learning synchronously in parallel. In particular, you will take a leading role in creating the simulation environment, 3D printing promising robot hand designs, and setting up a
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, Spatial Transcriptomics or proteomics/mass spectrometry would be advantageous as would experience using R and/or python code for bioinformatics data analysis of multiplex datasets. Knowledge of MATLAB, Perl
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culture by cell-to-cell fusion methods. Synergistic work tackling other key barriers in synthetic genomics will be done in parallel by postdocs in Oxford, Cambridge and Manchester that will be close