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Field
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software (e.g. ArcGIS, QGIS) and coding environments (e.g. Python or R), collaborating across LUMHR themes, and supporting interdisciplinary research activity. Teaching support may be required, up to a
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nearing completion) in Bioinformatics, Computational Biology, Genomics, or a related field, and proven experience in analysing high-throughput sequencing data. They will be proficient in R and/or Python and
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and to solve major global challenges. About you You will hold a relevant PhD/DPhil in Cognitive Neuroscience, Psychology, or a related subject. Strong programming skills (Python and/or MATLAB) and the
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pulse propagation, the material response, and other aspects of our experiments. Coding in Python Assisting with the filing and generation of intellectual property and technology transfer Assisting in
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Python and/or R are essential. Excellent communication skills and the ability to collaborate effectively in interdisciplinary research environments are also required. This is a full time, fixed term post
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
proficiency in Python is required, and hands-on experience with ML libraries such as PyTorch is expected. Theoretical understanding and experience with multi-physics modelling of electrochemical processes can
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of future research proposals. Skills, Experience & Qualifications Needed PhD in an appropriate subject area (awarded or submitted). Proficiency in Python for scientific computing, including basic scientific
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or a closely related field, and you will have a background in crystallisation and solid state characterisation. Knowledge of databases, and python coding is a must. The key responsibilities
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calibration and artefact mitigation. · Develop and maintain reconstruction and data-processing pipelines using Python programming tools. · Implement quantitative 3D analysis workflows such as
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skills in R and/or Python and experience in statistical modelling of high-dimensional biological data. Experience working in HPC environments and managing large-scale sequencing datasets is essential