Mathematical modelling of sex-differences in the human adrenal cortex and its disorders (DC11)

This PhD project is embedded in Work Package 3: Trustworthy Data and Models of the ENDOTRAIN network and aims to investigate in terms of computational modelling how glucocorticoid replacement therapies in female patients with adrenal hormone deficiency can be optimized to maintain or restore fertility. Fertility, influenced by hormones, environment, and psychology, is often reduced in these patients due to anovulation from unnatural hormone therapy. 

The PhD candidate will leverage multimodal datasets from dynamic hormone profiling and biosensors for ambulatory assessment to develop mathematical models of the Hypothalamic-Pituitary-Adrenal (HPA) axis and its cross-talk interactions with the Hypothalamix-Pituitary-Ovarian (HPO) axis. The project aims are to:   

• construct a mathematical model to delineate the type and strength of cross-talk mechanisms between the HPA and HPO axis and their interactions with tissue glucose, blood pressure and sleep,
• calibrate the model with wearable-based data (e.g., actigraphy, heart rate, temperature) and hormone profiles from healthy subjects and patients with primary adrenal insufficiency (PAI) under different treatment regimens,
• characterize inter- and intra-individual variability in model parameters to enable the generation of virtual patients,
• predict dynamic hormone profiles in healthy individuals and in patients with adrenal insufficiency,
• predict dynamic responses to glucocorticoid replacement therapy and compute optimal dosing regimens,
• contribute to the development of computer platforms that support the prevention, diagnosis and management of endocrine disease (endocrine digital twins),  

Participants will collaborate closely with other doctoral candidates and engage in secondments to technical and clinical partners within the consortium.  

Research Fields: 

Computational Biology, Applied Mathematics, Computer Science, Endocrinology, Digital Health, Medical Sensors, Systems Physiology  

Secondments: 

• Department of Computer Science, Sapienza University of Rom, Italy: to work on AI-based virtual twins (1 month)
• Department of Medicine IV, LMU University Hospital Munich, Germany: to calibrate the model with data collected by DC3 (1 month) 

University of Bergen (UiB), in collaboration with Haukeland University Hospital (HUH), offers a leading environment in clinical and translational endocrinology. The PhD candidate will be based at the Department of Informatics and will be member of the Computational Biology Unit (https://cbu.w.uib.no/). The Computational Biology Unit (CBU) works at the intersection of computer and life science. Its primary focus is on developing and implementing novel computational methods and apply these in the pursuit of fundamental biological questions. CBU is a cross-departmental research unit comprising groups from two different faculties at UiB - the Faculty of Science and Technology, and the Faculty of Medicine - all co-located at the Department of Informatics. CBU has members with diverse competences including experts in bioinformatics, biology, mathematics, computer science, chemistry, and medicine. The candidate will benefit from access to state-of-the-art high performance computing facilities, multimodal datasets, computational training, and world-leading international collaborations. 

The candidate should work full-time on the project and must not have resided or carried out their main activity (work, studies, etc.) in Norway for more than 12 months in the 36 months immediately before their recruitment date.  

The University of Bergen is subjected to the regulation for export control system. The regulation will be applied in the processing of the applications. 

Participation in the interdisciplinary training of the ENDOTRAIN network is mandatory, including workshops, retreats, transferable skills courses, and cohort-wide meetings across Europe. 

• Applicants must hold a master's degree or equivalent education in Mathematics, Physics, Computational Biology, Computer Science, Bioengineering or a closely related discipline with a strong mathematical/computational component. Master students can apply provided they complete their final master exam before 01.07.2026. It is a condition of employment that the master's degree has been awarded.
• Excellent programming skills (Python, Matlab or Julia) are a requirement.
• A solid background in mathematical modelling, preferably with ordinary and/or delay differential equations, is a requirement.
• Experience with model parameterisation, sensitivity analysis and optimisation is an advantage.
• Good dominion of time series analysis, dynamical systems theory and bifurcation analysis is an advantage.
• Strong interest in translational endocrinology, wearable device data and digital health technologies is an advantage.
• Applicants must be able to work independently and in a structured manner and have good communication skills and capacity for interdisciplinary collaboration.
• Applicants must have excellent command of written and spoken English. 

Personal and relational qualities will be emphasized. Research experience, ambitions and potential will also count when evaluating the candidates. 

We can offer: 

• a good and professionally stimulating working environment 
• salary as PhD research fellow (code 1017) in the state salary scale. This constitutes a gross annual salary of NOK 570 00. Further increases in salary are made according to length of service in the position.
• full social security coverage in Norway 
• travel and secondment budget included
• opportunities for international networking, industry exposure, and career development 

Applications will only be accepted through the Jobbnorge portal.

Applications without the mandatory attachments (application, CV, mobility declaration, motivation templates) will not be considered.

Informal inquiries may be directed to Professor Susanna Röblitz (susanna.roblitz@uib.no)