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PhD studentship in Predicting Higher-Order Biomarker Interactions using Machine Learning

The University of Edinburgh

Qualification Type: PhD
Location: Edinburgh
Funding for: UK Students, EU Students, International Students
Funding amount: Full time PhD tuition fees for a student with a Home fee status (£4,500 per annum) or Overseas fee status (£24,700 per annum).
Hours: Full Time, Part Time
Placed On: 14th January 2022
Closes: 15th February 2022
 

One fully funded PhD position to work with Dr Ava Khamseh in the School of Informatics at the University of Edinburgh, on a project titled “Predicting Higher-Order Biomarker Interactions using Machine Learning”.

This cross-disciplinary BBSRC PhD studentship with industrial collaboration with GSK would suit an ambitious individual from a physics, mathematics, statistics, computer science (or similar) quantitative background with an interest in biomedical applications.

There is an opportunity for a 3-6 month industry placement at the GSK AI hub in London. 

The problem of inferring pair-wise and higher-order interactions in complex systems involving large numbers of interacting variables appears in many contexts in biology, and has become accessible due to real and simulated high-throughput data being generated in recent years.

Full project outline: www.ed.ac.uk/informatics/postgraduate/fees/research-scholarships/research-grant-funding/phd-studentship-predicting-higher-order-biomark

Project: The student will develop novel state-of-the-art methods, that integrate mathematical statistics and machine learning, to quantify higher-order interactions amongst large numbers of variables and/or with relation to biomedical outcomes and phenotypes. The student will apply the methods on simulated and real biomedical data such as single-cell gene expression dataor biomarker and trait data from the UK Biobank.

During the course of this PhD the student will develop skills in Causal Inference, Neural Networks, Ensemble Learning, Targeted Learning, Applying quantitative methodologies to high-throughput biological data (e.g., single-cell RNA-seq or the UK Biobank), Scientific writing and presentation and working closely with industry partner.

Website edbiomed.ai

Candidate’s profile

  • Minimum of 2:1 in first degree and/or Master’s degree in physics/ mathematics/statistics/computer science or similar.
  • Proficiency in English (both oral and written).
  • Advanced programming skills (Python, Pytorch or equivalent).
  • Excellent verbal and written communication skills, both in terms of informal discussion and formal presentations.
  • Biomedical motivation.
  • Ability to work effectively and efficiently in a team.

Studentship and eligibility

The studentship starting in the academic year 2021/22 covers:

  • Full time PhD tuition fees for a student with a Home fee status (£4500 per annum) or Overseas fee status (£24,700 per annum).
  • A tax free stipend of GBP £15,609 per year for 4 years.
  • Additional programme costs of £1000 per year.

Application Information

Applicants should apply via the University’s admissions portal (EUCLID) and apply for the following programme: Informatics: ANC: Machine Learning, Computational Neuroscience, Computational Biology via the following link www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&edition=2021&id=489 with a start date of 12th September 2022

Applicants should state “Predicting Higher-Order Biomarker Interactions using Machine Learning” and the research supervisor (Dr Ava Khamseh) in their application and Research Proposal document.

Complete applications submitted by 15 February 2022 will receive full consideration; after that date applications will be considered until the position is filled. The successful candidate will be anticipated to start ASAP and not later than September 2022 (depending on individual circumstances).

Applicants must submit:

  • All degree transcripts and certificates (and certified translations if applicable).
  • Evidence of English Language capability (where applicable).
  • A short research proposal (max 2 pages).
  • A full CV and cover letter describing your background, suitability for the PhD, and research interests (max 2 pages).
  • Two references (note that it the applicant’s responsibility to ensure reference letters are received before the deadline).

Only complete applications (i.e. those that are not missing the above documentation) will progress forward to Academic Selectors for further consideration.

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