At a Glance
- Tasks: Lead innovative research in Digital Endocrinology and model hormonal rhythms.
- Company: Join a dynamic team at a leading university focused on cutting-edge biomedicine.
- Benefits: Enjoy a fantastic pension scheme, health services, and generous leave entitlements.
- Why this job: Make a real impact in healthcare by developing personalised models for hormone management.
- Qualifications: Experience in mathematical modelling and familiarity with dynamical systems and Bayesian inference.
- Other info: Flexible working options and a supportive, collaborative environment await you.
The predicted salary is between 36000 - 60000 ÂŁ per year.
We are excited to offer this opportunity to a highly motivated Research Associate to join an ambitious new team working in the field of Digital Endocrinology. The successful applicant will conduct original research aimed at understanding variability in circadian hormone profiles in humans, uncovering fundamental insights into endocrine function and supporting the diagnosis and management of endocrine conditions.
The position is funded through a UKRI Future Leaders Fellowship awarded to Dr Eder Zavala: “Developing a mathematical and computational framework for digital endocrinology”. The work will build on hormonal time series data collected at unprecedented time resolution in healthy humans and in patients, including studies in real‑life settings with a state‑of‑the‑art wearable device.
We are seeking to appoint a creative and enthusiastic individual who will lead on projects modelling the mechanisms behind hormonal rhythm misalignment. The models will be used to predict dynamic responses to stressors, sleep disruptions, and diagnostic tests, as well as the long‑term changes that occur during disease. A key challenge of your work will be accounting for the variability in hormonal rhythms observed across individuals. To characterise this variability, you will take advantage of unique datasets and advanced mathematical and computational methods to help develop personalised models. The practical element of your project will be based on, but not limited to, Ordinary and Delay Differential Equations, bifurcation analyses, Bayesian inference, and Machine Learning methods.
In addition to leading your own research projects, the appointed candidate will have the opportunity to contribute to the projects of PhD students in the group, as well as with an international team of collaborators including clinicians, computer scientists, and mathematicians. The team is dynamic and ambitious as well as being welcoming and supportive.
The applicant should have experience with mathematical modelling using ODEs and/or DDEs, parameter estimation methods and HPC simulations. The applicant should be familiar with dynamical systems theory, network analysis and Bayesian inference frameworks.
Vacancy: full‑time post available from 1st April 2026 with a contract of 36 months.
What you will get in return:
- Fantastic market‑leading pension scheme
- Excellent employee health and wellbeing services including an Employee Assistance Programme
- Exceptional starting annual leave entitlement, plus bank holidays
- Additional paid closure over the Christmas period
- Local and national discounts at a range of major retailers
As an equal‑opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
Our University is positive about flexible working – hybrid working arrangements may be considered.
Due to the number of applications we unfortunately may not be able to provide individual feedback on your application. We are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies. Any recruitment enquiries from recruitment agencies should be directed to People.Recruitment@manchester.ac.uk. Any CVs submitted by a recruitment agency will be considered a gift.
Enquiries about the vacancy, shortlisting and interviews:
Name: Dr Eder Zavala – UKRI Future Leaders Fellow
Email: eder.zavala@manchester.ac.uk
General enquiries:
Email: People.recruitment@manchester.ac.uk
Technical support: 0161 850 2004
This vacancy will close for applications at midnight on the closing date.
Please see the link below for the Further Particulars document which contains the person specification criteria.
Research Associate Mathematical Biomedicine in Manchester employer: The University of Manchester
Contact Detail:
The University of Manchester Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Associate Mathematical Biomedicine in Manchester
✨Tip Number 1
Network like a pro! Reach out to professionals in the field of mathematical biomedicine, especially those working in digital endocrinology. Attend relevant conferences or webinars and don’t be shy to introduce yourself and share your interests.
✨Tip Number 2
Prepare for interviews by brushing up on your knowledge of ODEs, DDEs, and Bayesian inference. Be ready to discuss how you can apply these methods to real-world problems in endocrine function. We want to see your passion and expertise shine!
✨Tip Number 3
Showcase your projects! If you've worked on any relevant research or modelling projects, make sure to highlight them during interviews. Bring along examples that demonstrate your skills in handling hormonal time series data and computational methods.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re serious about joining our dynamic team. Don’t forget to tailor your application to reflect your enthusiasm for the role and the exciting work we do!
We think you need these skills to ace Research Associate Mathematical Biomedicine in Manchester
Some tips for your application 🫡
Show Your Passion: When writing your application, let your enthusiasm for mathematical biomedicine shine through! We want to see how excited you are about the research and the impact it can have on understanding endocrine conditions.
Tailor Your Experience: Make sure to highlight your relevant experience with ODEs, DDEs, and any other mathematical modelling techniques. We’re looking for specific examples that demonstrate your skills and how they relate to the projects you'll be working on.
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon unless it's necessary. Remember, we want to understand your qualifications without getting lost in complex language!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way to ensure it gets to us directly. Plus, you’ll find all the details you need to make your application stand out.
How to prepare for a job interview at The University of Manchester
✨Know Your Maths Inside Out
Make sure you brush up on your knowledge of Ordinary and Delay Differential Equations, as well as parameter estimation methods. Be ready to discuss how you've applied these concepts in past projects or research, as this will show your practical understanding and readiness for the role.
✨Showcase Your Research Experience
Prepare to talk about any previous research you've conducted, especially if it relates to hormonal rhythms or mathematical modelling. Highlight specific projects where you used advanced mathematical and computational methods, and be ready to explain your thought process and findings.
✨Familiarise Yourself with the Team's Work
Take some time to read up on Dr Eder Zavala's research and the projects currently being undertaken by the team. This will not only help you understand their goals but also allow you to ask insightful questions during the interview, demonstrating your genuine interest in joining their ambitious group.
✨Prepare for Collaborative Discussions
Since the role involves working with an international team of clinicians, computer scientists, and mathematicians, think about how you can contribute to collaborative projects. Be ready to discuss your teamwork experiences and how you approach interdisciplinary work, as this will be key to succeeding in this dynamic environment.