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routinely collected healthcare data. Through the analysis of linked electronic health records, you will generate evidence to inform clinical guidelines, optimise prescribing practices and support healthcare
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to develop, simulate, and critically evaluate mechanistic models rather than relying solely on descriptive data analysis. Scientific programming ability in Python, MATLAB, Julia, C/C++, or a comparable
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Qualifications Experience with analysis of three-dimensional velocity fields Computer programming/scripting experience Experience with graph analysis and optimization methods Required License/Registration
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-VIS-NIR). Experience in hyperspectral data processing (HMSPL, μXRF, μXAS) and statistical analysis (clustering, machine learning) is preferred. Familiarity with fossilization processes and taphonomic
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dynamometry. Develop and maintain analysis pipelines and ensure data quality across sessions. Prepare manuscripts, conference abstracts, and grant applications as first author and contributing author. Develop
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, with the possibility of renewal, in the field of Analysis and Partial Differential Equations (PDEs). Job description - The successful candidate will collaborate on research projects related
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proteomics, advanced image analysis, and computational approaches to investigate molecular and cellular heterogeneity and to integrate spatial molecular information with histopathological and clinical data. We
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experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and components to automate
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confocal microscopy, and quantitative fluorescence imaging, to monitor and quantify protease activity in real time. 4. Data analysis and interpretation Processing, analyzing, and interpreting experimental
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measurements, including confocal microscopy, and quantitative fluorescence imaging, to monitor and quantify protease activity in real time. 4. Data analysis and interpretation Processing, analyzing, and