EPSRC Case Conversion Scholarship University of Sheffield, UK

Gates Cambridge Scholarship Programme for PhD in Cambridge, UK

EPSRC Case Conversion Scholarship: Join the University of Sheffield’s Information School for an exciting PhD opportunity funded by an EPSRC CASE Scholarship in collaboration with Syngenta Crop Protection. We are seeking enthusiastic UK candidates to conduct research in the area of Interpretable Machine Learning Algorithms for Predictive (Eco-)Toxicology. This fully funded 4-year studentship offers an enhanced stipend along with valuable placement experience.

  • Designation: PhD Researcher
  • Research Area: Interpretable Machine Learning Algorithms for Predictive (Eco-)Toxicology
  • Location: Sheffield
  • Eligibility/Qualification: Minimum 2.1 undergraduate honours degree and/or MSc degree in a relevant Science or Engineering subject. Interest in machine learning or artificial intelligence and computer programming skills are advantageous.
  • Funding: £18,662 per annum (basic UKRI rate) plus an additional £4,000 per annum, covering Home tuition fees and research/training support grant.

Job Description: The project focuses on developing accurate and interpretable machine learning prediction methods for molecular design in the pharmaceutical and crop protection sectors. While complex algorithms have shown promise, this research aims to create models that offer both high prediction performance and interpretability. The project also extends to interspecies predictions to aid chemists in understanding species sensitivity, crucial in avoiding late-stage attrition. The successful candidate will have the opportunity to work collaboratively with Syngenta Crop Protection during a placement period.

How to Apply: For detailed entry requirements, information about the research, and the application process, please visit our department’s webpages at www.sheffield.ac.uk/is/phd.

Last Date for Apply: 1st October 2023

This scholarship is a unique chance to make a significant impact on predictive toxicology through cutting-edge machine learning research. If you are a motivated candidate with a passion for advancing this field, we encourage you to apply before the closing date.

Placed On: 23rd August 2023 Closes: 1st October 2023



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