PhD Research Fellow in Mathematical Modelling of Adrenal Gland Steroid Biosynthesis (ENDOTRAIN) University of Reading in United Kingdom

The University of Reading is offering a fully funded PhD Research Fellow position in Mathematical Modelling of Adrenal Gland Steroid Biosynthesis as part of the Marie Skłodowska-Curie Doctoral Network (ENDOTRAIN). This prestigious opportunity is embedded within a European-wide initiative to advance digital endocrinology, integrating AI, sensor technology, omics, and clinical medicine to transform the diagnosis and treatment of adrenal diseases. The position is funded by the European Commission and coordinated by the University of Bergen, Norway.

Supervised by Dr. Zuowei Wang, Dr. Eder Zavala, and Prof. Marcus Tindall, the successful candidate will join the Department of Mathematics & Statistics at the University of Reading, a leading research environment with a strong track record in applied mathematics, data science, and biomedical engineering. The department boasts state-of-the-art high performance computing facilities and international collaborations, with 98% of research outputs ranked as world leading or internationally excellent.

The PhD project focuses on developing and validating mathematical models to study the dynamics of adrenal gland steroid biosynthesis and their spatial relationship with adrenal tumours, particularly in Primary aldosteronism (PA) and Mild autonomous hypercortisolism (MACS). The research will involve creating reaction-diffusion models, implementing numerical simulation codes, and analyzing spatiotemporal profiles of steroid synthesis using imaging data from ENDOTRAIN partner groups. The project aims to provide quantitative insights into enzyme-mediated synthesis, pathway signalling dysregulation, and the impact of spatial disturbances such as tumours on hormone expression levels. Additionally, the candidate will contribute to the development of computer platforms for endocrine digital twins, supporting prevention, diagnosis, and management of endocrine diseases.

Participants will collaborate closely with other doctoral candidates and undertake secondments at technical and clinical partners, including Evangelismos General Hospital Athens, Ludwig-Maximilians-Universität München, and the University of Manchester. These placements will provide opportunities to collect patient data, inform model development, and test outcomes using experimental data.

Applicants must hold a Master’s degree (or equivalent) in Mathematics, Physics, Bio- or Biomedical engineering, or a closely related discipline with a strong mathematical component. Required skills include mathematical modelling, ordinary and partial differential equations, dynamical systems, numerical analysis, and programming (Python, Matlab, Fortran, or C/C++). Experience in numerical solutions of differential equations and knowledge of statistics and probability are desirable. Candidates should demonstrate a strong interest in translational endocrinology, wearable device data, and digital health technologies, as well as excellent English communication skills.

Eligibility criteria include not having resided or carried out a main activity in the UK for more than 12 months in the past 36 months before the PhD start date, not already holding a doctoral degree, and providing documentation of the awarded master's degree or a statement of expected completion before the position start date. The programme encourages applications from women, people with immigrant backgrounds, and people with disabilities, in line with its gender equality and diversity goals.

The position offers a competitive salary (£42,978–£46,974 per annum, depending on eligibility for family allowance), a mobility allowance, and a family allowance if applicable. The duration is three years, with travel and secondment budgets included, and opportunities for international networking, industry exposure, and career development.

Applications must be submitted via the Jobbnorge portal, including all mandatory attachments: application form, CV, mobility declaration, and motivation letter. For further details, visit the project and programme webpages or contact the supervisors and programme manager as listed in the advertisement.