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Field
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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
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systems using tools such as MATLAB, Python, COMSOL Multiphysics, Lumerical, or equivalent platforms. Contribute to experimental laboratory work in ultrafast and nonlinear optics. Develop and optimise
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in Python Well-developed research skills Experience or knowledge of computational best practices Experience of training staff Evidence of preparing publications for specialist/ general science journals
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in machine learning and/or advanced analytical methods, experience working with complex or large-scale datasets, and strong programming skills (e.g., Python or R). You will be able to communicate
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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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statistical and machine learning methods, particularly for high-dimensional or longitudinal data. Demonstrable experience in research software development using R or Python and a track record of contributing
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simulation and enhanced sampling techniques. Candidates must be proficient in Python and familiar with version control platforms such as GitHub. You should demonstrate a strong research track record through
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will lead the programming of R/Python packages for the analysis as well as adapt existing and develop new research methodologies and training materials. You will report research findings in the form
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(essential) extensive experience in programming in Python or Matlab and data analysis (essential) experience with behavioural and neuroimaging (fMRI, M/EEG) data design/collection/analysis (essential) track
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and data processing skills: experience of programming in one or more languages (e.g. R, C/C++, Python, Matlab). Practical experience of algorithm development and implementation of machine learning