Research Associate in Causal Inference

About the role

This is an outstanding opportunity for a Research Associate with a background in statistics, biostatistics, data science, computer science, applied mathematics, or a related quantitative discipline to work within the reirradiation research team under the supervision of Dr Vasquez Osorio. The post holder will be part of the Radiotherapy Related Research (RRR) group, based at the Paterson Building in Withington.

The candidate will support the aims of Dr Vasquez Osorio’s UKRI Future Leaders Fellowship on Advancing Reirradiation and Treatment Outcome Understanding with Robust Dose Mapping and Causal Inference. This innovative project will study how advanced spatiotemporal causal inference methods can be developed and applied to complex radiotherapy datasets to understand the relationship between radiation dose and treatment outcomes, enabling improved evidence-based decision-making for patients undergoing reirradiation.

This position offers an ideal opportunity for an enthusiastic, well-motivated individual to work as part of a sociable multidisciplinary team consisting of physicists, computer scientists, clinicians, radiographers, and imaging experts. The ideal candidate will have excellent interpersonal, communication, programming, and organisational skills, and will be able to work with minimal supervision.

Responsibilities

• Developing research objectives and proposals for own or joint research, with the assistance of a mentor if required.
• Conducting individual and collaborative research projects.
• Writing up research work for publication.
• Communicating complex information, orally, in writing and electronically.
• Using creativity to analyse and interpret research data and draw conclusions on the outcomes.

Requirements

Essential Criteria

• Having, or being about to obtain, a relevant PhD (or equivalent).
• Experience in advanced statistical modelling and/or causal inference methods.
• Ability to develop or adapt novel computational or statistical methods (beyond application of existing techniques).
• Strong programming skills (e.g., Python and/or R).
• Knowledge of radiotherapy, cancer research, medical imaging, or clinical data analysis.

Desirable Criteria

• Experience analysing complex biomedical, healthcare, or spatiotemporal datasets.
• Experience with spatiotemporal methods.
• Track record of presenting at national and international conferences.

We value transferable skills and real-world experience as much as formal qualifications.

Benefits

• Generous employer contribution pension.
• 29 days annual leave plus bank holidays, along with Christmas closure.
• Ride to work and EV car scheme available.

For more information, see University of Manchester Benefits and Flexible and Hybrid working.