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information on complex diseases. The goal of our efforts is to build and apply automated analytical pipelines for various types of pathology data, including histopathology images and multi-omics (e.g., genomics
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Your Job: This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining
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associated partners including six from industry and two start-ups. More information may be found on MICROSUNSET . To be eligible for a MICROSUNSET PhD-position, you must not have lived, studied, or worked in
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to the following: SDSC6012 – Time Series and Recurrent Neural Networks SDSC6016 – Predictive Analytics and Financial Applications SDSC8013 – Statistical Methods for Categorical Data Analysis (For detailed course
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Application deadline: All year round Research theme: Environmental geochemistry How to apply: https://uom.link/pgr-apply-2425 This 3.5-year PhD studentship is open to EU, UK, and US applicants. The
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Master’s degree (or equivalent) in a relevant discipline such as computer science, mathematics, physics, or data science. They should have strong analytical skills related to statistics, machine learning
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, engineers, and non-technical stakeholders LanguagesENGLISHLevelExcellent Additional Information Website for additional job details https://www.qsarlab.com/dolacz-do-zespolu/ Work Location(s) Number of offers
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. Funding Notes This project is for Self-funded students or students with external funding. References https://gravity-cosmology.sites.sheffield.ac.uk/phd-projects https://sites.google.com/a/sheffield.ac.uk
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plan by conducting experiments, sample and data analysis, and write up of results for scientific publication are part of the PhD process – a journey to become an independent researcher! Throughout
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analytics (chromatography, Raman spectroscopy, Surface Plasmon Resonance, etc.) Data analysis Machine learning Additional Information Work Location(s) Number of offers available1Company/InstituteUniversité