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reporting of SMART trials. What we are looking for This PhD is suitable for a UK resident with: Strong analytical and programming skills (e.g. R, Stata or similar) A Degree or Master’s with a substantial
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engineering and downstream analytical workflows. While experience in machine learning is welcome, you should have a background in strong data engineering, data management, and analytical skills, and sufficient
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including by securing external funding and delivering successful research outcomes. You will be expected to demonstrate the following key requirements: PhD in Cell Biology High level analytical capacity
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of Bioengineering. You will work at the interface of academic research and early-stage commercial translation, embedding advanced analytical methods from the Statistical Physics of Cognition project into applied
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Imperial College London invites applications for the following two positions: Assistant Professor in Health Data Analytics Assistant Professor in Environmental Epidemiology and Data Science This is
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of workflows. In addition, you will provide training to PhD students, postdocs, and new lab members in chemoproteomics workflows from sample preparation through to data analysis, helping to build capability and
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healthcare interventions. You will design and deliver cost-effectiveness and budget impact analyses, analyse healthcare cost and outcomes data, and develop decision-analytic models. Working closely with
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to develop a high-performance disposable analytical tool for the on-site detection of folic acid in several food matrices. This technology is based on our recently developed wireless and batteryless, near
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candidate will drive the development and application of advanced diagnostic and analytical tools, set new standards in immunological and genomic epidemiology, and contribute to global arbovirus response. PhD
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spectroscopy (D) Experience of data analysis from a wide variety of analytical techniques including, but not limited to, X-ray diffraction, Electron microscopy, Electrochemical cell testing, Thermal analysis