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of multiple long-term conditions resulting in increased risk of hospitalisations, polypharmacy and/or mortality using population-scale, linked, electronic health records from the Secure Anonymised Information
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: Full Time Closes: 7 January2026, 23:59 GMT Application link: https://www.essex.ac.uk/postgraduate/research/doctoral-training-partnerships/aries ARIES (Advanced Research and Innovation in
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, migration, invasion, and genetic instability. Although PBF’s primary function remains unclear, our recent data reveal a physiological role in cell adhesion and motility. Tyrosine phosphorylation is a key
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-of-the-art technology (including hydrogen-deuterium exchange, cross-linking, top-down and native mass spectrometry). After this, the technology advancement will be applied to solve a variety of complex
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unparalleled, powerful genetics. With our findings and collaborative approach to discuss with psychologists, we will strive to link our findings to understanding human conditions. The approach will combine a
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-informed learning) with hard physical constraints (Navier–Stokes in spectral space) we will develop methods to super-augment experimental data via data assimilation and turn sparse wind-tunnel measurements
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turbulent flows, as their mean strain is varied from high to low levels, and will document the failure of classical theories to describe intermediate strain regimes. The produced data-set will be utilized
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stability as system strength continues to decline. Building on existing frameworks such as the WECC Composite Load Model (CLM), you will develop and validate data-driven methods for load identification and
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intensity of these changes. This PhD project will ultimately enable aircraft to reroute safely and efficiently in real time as weather evolves. By merging scientific machine learning, large-scale data
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mucosa. Objectives include: 1. Training generative models to produce realistic, high-resolution microscopy images. 2. Ensuring no synthetic image is identical to real patient data. 3. Evaluating image