Research Associate in Mathematical Biomedicine
Join a pioneering team to develop computational models for precision endocrine medicine using wearable health data.
Best for:Best for computational biologists, applied mathematicians, or data scientists with expertise in time series analysis, Bayesian methods, and differential equation modelling, seeking to contribute to precision medicine research.
Overview
This position offers a Research Associate opportunity at The University of Manchester to advance digital endocrinology through mathematical and computational modelling. The successful candidate will lead data-driven research projects focused on circadian hormone profiles, leveraging high-resolution wearable device data to uncover insights for endocrine condition diagnosis and management. The role involves time series analysis, Bayesian inference, machine learning, and computational simulations of differential equations, with collaboration across interdisciplinary teams including clinicians, computer scientists, and mathematicians. The project is funded by a UKRI Future Leaders Fellowship and aims to develop personalised models accounting for individual variability in hormonal rhythms. The team is dynamic, supportive, and based in Manchester’s School of Medical Sciences.
What you'll do
- Lead computational data analysis projects to characterise variability in circadian hormone profiles using high-resolution wearable device data.
- Develop personalised mathematical models to predict dynamic endocrine responses to stressors, sleep disruptions, and diagnostic tests.
- Apply advanced techniques such as time series analysis, network analysis, Bayesian inference, and machine learning to hormonal time series datasets.
- Collaborate with interdisciplinary teams including clinicians, computer scientists, and mathematicians to integrate computational models with real-world endocrine data.
- Contribute to mentoring PhD students and supporting international research collaborations.
- Conduct computational simulations of mathematical models using differential equations and high-performance computing (HPC).
Requirements
Degree required: Master’s degree or equivalent in a relevant field such as Mathematics, Computer Science, Biomedical Engineering, or a related discipline.
Language: English; proficiency required for academic communication and collaboration.
Instruction: English
- High proficiency in scientific computer programming using Python, Julia, R, or MATLAB.
- Experience with parameter estimation methods and high-performance computing (HPC) simulations.
- Familiarity with dynamical systems theory and mathematical modelling using ordinary differential equations (ODEs) or delay differential equations (DDEs).
- Strong background in time series analysis and machine learning techniques.
- Open to all eligible candidates; no specific nationality or residency restrictions mentioned.
Funding & terms
How to apply
Deadline: 2026-10-23
Applicants must submit their CV and a cover letter through the University of Manchester’s job application portal. Ensure your application highlights your relevant skills in computational modelling, time series analysis, and programming proficiency. The application process includes shortlisting and interviews.
- CV
- Cover letter
Contact
Dr Eder Zavala· UKRI Future Leaders Fellow; Senior Lecturer in Mathematical Biomedicine
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