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competence is expected in the use of analytical methods including proton NMR spectroscopy and mass spectrometry for reaction monitoring. Ideally candidates will also be experienced in synthetic organic
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analytical methods. Experience working with Mycobacterium tuberculosis or other Class 3 pathogens is essential, particularly in conducting infection studies using cell lines or clinical samples and in drafting
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to carbon dioxide in 1983. However, the carbamate PTM is unstable outside the cell, and its identification presents significant analytical challenges. Several stable carbamates have been identified in protein
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of Biodiversity Change’, in collaboration with colleagues at LCAB and Maria Dornelas at the University of St. Andrews. Role We are seeking an enthusiastic Post-Doctoral Research Associate (PDRA) with analytical
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on super-resolution fluorescence and CryoET and will need to analyse data. We encourage applicants with a broad experimental-analytical interest, as the job holder will contribute both to the development
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of the continuum equations governing the mechanics of fluids and soft solids. Ability to design theoretical models and carry out appropriate analytical calculations to achieve the proposed objectives. Experience in
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neurogenetics of Drosophila. This requires expertise in setting up and analysing behavioural tests, using appropriate analytical methods and software and skills in coding in e.g. R, MatLab, R, Python, as required
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of this position within the team is the experimental work on decontamination of porous materials, although it is also expected that the successful candidate will contribute strongly to the analytical and numerical
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in designing and operating combustion test rigs; developing analytical engineering models using CONVERGE or other CFD tools; and conducting experimental studies in a mechanical engineering laboratory
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physics (e.g., Turing patterns). This will involve: (i) developing new analytical/theoretical tools for the study of reaction-diffusion systems, (ii) performing large scale, machine-learning-assisted