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leading clinicians and researchers tackling real‑world challenges in critical care, data innovation, AI and advanced analytics. We are seeking a talented Data Scientist / Clinical Data Engineer
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Generative AI model which will predict the fertility stages of species based on analytical information from blood (or body fluid) samples. The successful candidate will work closely with the project Chief
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culture and differentiation • Experience in biomaterials, muscle biology and advanced microscopy • A strong publication record and analytical capability • Excellent organisational and communication skills
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biology approaches, including single particle cryogenic electron microscopy Analyse data and prepare figures, manuscripts and research reports Contribute to the development of grant applications and funding
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: • Experience undertaking data analysis in R, STATA or SAS • Skills in economic evaluation, epidemiological analysis or health services research • Analytical and manuscript preparation capability with early
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, biophysical, structural and spectroscopic techniques Operate advanced laboratory and imaging equipment, including cryoEM-related technologies Contribute to data analysis, figure preparation and scientific
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applications, and the collection of qualitative and quantitative data Prepare manuscripts for publication and contribute to competitive grant applications Support the Group Lead with the coordination of funded
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hold a PhD in chemical biology or a related life sciences discipline. Knowledge of metabolic and fibrosis indications would be advantageous. Please refer to the selection criteria for a complete listing
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. The successful candidate will hold a PhD in organic or medicinal chemistry discipline. Knowledge of metabolic and fibrosis indications would be advantageous. Please refer to the selection criteria for a complete
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, characterization and evaluation of novel PfM1 and M17 aminopeptidase inhibitors. This would include compound design, data management and analysis and sample preparation as well as a range of drug discovery-related