Research Fellow (Biostatistician /Machine Learning Scientist)

The University of Edinburgh; City of Edinburgh; Hybrid; GBP 40,000 - 49,000; Full time

Job summary

The University of Edinburgh seeks a Research Fellow in Biostatistics or Machine Learning to join a collaborative team at the Usher Institute. The successful candidate will develop statistical methods to analyze complex biological data, focusing on biomarkers in rheumatoid arthritis. This 24-month fixed-term position offers the opportunity for professional growth in a dynamic research environment.

Benefits

Competitive salary and staff benefits

Opportunities for professional development

Inclusive and diverse working environment

Qualifications

• PhD or equivalent experience in a relevant field.
• Proficiency in R and Python, experience with Unix systems.
• Excellent communication and teamwork skills.

Responsibilities

• Develop statistical and machine learning methods for biological data.
• Collaborate with teams to translate genetic findings into clinical insights.
• Contribute to publications and presentations.

Skills

Probabilistic models

Bayesian inference

Machine learning

Communication

Teamwork

Education

PhD in machine learning, genetic epidemiology, or a related numerate discipline

Tools

R

Python

Unix systems

GitHub

Rmarkdown

Job description

Research Fellow (Biostatistician / Machine Learning Scientist)

Join to apply for the Research Fellow (Biostatistician / Machine Learning Scientist) role at The University of Edinburgh.

Job Details

• Grade UE07: £40,497 - £48,149 per annum
• Location: CMVM / MGPHS / USHER Institute / Edinburgh Bioquarter
• Full-time: 35 hours per week
• Fixed-term: 24 months (with possibility of extension)
• Hybrid working considered, with at least 60% on campus

Job Description

The Centre for Population Health Sciences at the Usher Institute, University of Edinburgh, seeks a skilled biostatistician, informatician, or data scientist to work on identifying biomarkers of treatment response in rheumatoid arthritis, utilizing complex genetic, transcriptomic, and proteomic data linked to health outcomes from large biobanks and clinical cohorts.

The successful candidate will develop and advance statistical methods for gene discovery, contributing to biomarker validation and prediction of disease progression and treatment response. This role is part of Dr. Spiliopoulou’s career development fellowship funded by Versus Arthritis, collaborating with academic, clinical, and industry partners.

Key responsibilities include:

1. Developing and applying statistical and machine learning methods to high-dimensional biological data.
2. Collaborating with multidisciplinary teams to translate genetic discoveries into clinical insights.
3. Contributing to publications and presentations.
4. Engaging with external collaborators and stakeholders.

Qualifications and Skills

• PhD in machine learning, genetic epidemiology, or a related numerate discipline, or equivalent experience.
• Knowledge of probabilistic models, Bayesian inference, and machine learning.
• Proficiency in R, Python, or both; experience with Unix systems and reproducible research tools (e.g., GitHub, Rmarkdown).
• Excellent communication and teamwork skills.
• Ability to manage project timelines effectively.

Application Process

Please submit your CV and a supporting statement explaining how you meet the required skills and experience. For external applicants, refer to the application guide. Internal applicants should apply via the People and Money system.

Benefits and Additional Information

• Competitive salary and staff benefits.
• Opportunities for professional development.
• Inclusive and diverse working environment.
• Application deadline: 1st July 2025, 11:59 pm GMT.

Equal Opportunities

The University of Edinburgh values diversity and is committed to equality. We welcome applications from all backgrounds.

Note

This job posting is active; it is not expired.