PhD in Mathematical modelling and endocrinology in Manchester
Deadline: 18/02/2026 Project title: Mathematical modelling of the cross-talk interactions between the HPA axis and the renin-angiotensin system Hormonal concentrations are regulated by endocrine axes and are the perfect example of complex regulatory systems involving multiple levels of organisation. Endocrine regulation is highly dynamic, with hormone levels exhibiting complex periodic behaviour over short and long timescales (e.g., hourly, daily and monthly rhythms). These systems also exhibit nonlinear responses to perturbations, typically mediated by feedback loops involving multiple components and crosstalk interactions with other endocrine systems. At the same time, endocrine systems exhibit trade-offs between sensitivity and robustness, which allows adaptability to physiological challenges. Importantly, dysregulation of these dynamic processes can lead to disease.
The PhD candidate will leverage multimodal datasets from dynamic hormone profiling (e.g., U-RHYTHM) (3) and biosensors for ambulatory assessment to develop mathematical models of the Hypothalamic-Pituitary-Adrenal axis and its cross-talk interactions with the renin-angiotensin system. The project aims are to:
- Predict dynamic hormone profiles in healthy individuals and in patients with endocrine conditions (PA, MACS, PAI and CAH),
- Predict dynamic responses to physio- pathological stimuli, from sleep disruptions to clinical tests such as dexamethasone suppression tests and responses to salt loading,
- Integrate hormonal and wearable device data (e.g., actigraphy, heart rate, temperature) with endocrine test outcomes to optimise diagnosis,
- Compute optimal dosing time points of hormonal replacement therapy,
- Contribute to the development of computer platforms that support the prevention, diagnosis and management of endocrine disease (endocrine digital twins),
The PhD position is embedded in Work Package 3 (Trustworthy Data and Models) of the EU Horizon ENDOTRAIN doctoral training network. Participants will collaborate closely with other Doctoral Candidates and engage in secondments to technical and clinical partners within the consortium.
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