Parveen Gartan and Belén García Pascual have been awarded a PhD internship from Digital Life Norway. Parveen is a PhD student at the Dpt of Chemistry and CBU in the Reuter group and will spend three-months at Astra Zeneca. Belén is at the Dpt of Mathematics, in the CBU-affiliated Stochastic Biology Group and will spend her internship at DNV. CBU is proud and excited to see Parveen and Belén going to explore opportunities outside the academic “bubble”.

CEDAS, the Center for Data Science at the University of Bergen, and NORBIS, the Norwegian Research School in Bioinformatics, Biostatistics, and Systems Biology, invite both starting and experienced data scientists to an exciting joint summer school on data science and its biomedical applications from 7-11 August 2023.

The summer school will be held in Bergen at Hotel Zander K. It is mainly intended for PhD candidates, but also Postdocs and other researchers interested in learning more about data science are welcome to attend. As data science is relevant across a wide range of disciplines, we hope that we will be able to attract a diverse group of participants.

The summer school will feature keynotes, theoretical and practical sessions on Machine Learning, Statistics, and Visual Data Science, as well as a social program.

For more information, please visit the official summer school website: Joint CEDAS-NORBIS Summer School 2023

A Modelling-Based Investigation Of The Dynamic Behavior Of Reproductive Hormones In Girls And Wom

On May 5th Sophie Fischer-Holzhausen successfully defended her PhD thesis titled: A matter of timing – A modelling-based investigation of the dynamic behavior of reproductive hormones in girls and women. Female health is an important but often understudied field of human health. Consequently, a considerable number of individuals of reproductive age experience one or more menstrual disorders in ways that effects their abilities to work and contribute (https://news.mit.edu/2022/events-illuminate-critical-need-menstruation-science-1003). Understanding the endocrine control of the female reproductive system is one element that will help us to improve this situation. Hereby, mathematical models provide us with concepts to integrate the knowledge we have about the endocrine control of menstrual cycle in a systematic way, help to understand complex regulation processes and predict – with some uncertainty – the effect of treatments. In her thesis Sophie presents mechanistic models describing the endocrine regulation of the female reproductive system with focus on the hypothalamic-pituitary-gonadal axis (HPG axis). Her scientific work is presented in three research articles. The first two publications present a mechanistic model of the mature HPG axis and its interactions with the process of follicle maturation (folliculogenesis). The model parameters are investigated and the application of the model to predict the effects of hormonal medication is demonstrated. In a third scientific publication, Sophie proposes a Bayesian updating workflow that allows the incorporation of cross-sectional data in the model calibration routine.