14 Postdoctoral Fellowship at University of Bergen, Bergen, Norway

The project will focus on understanding what controls the spatial and temporal variations in magmatic output during formation of the North Atlantic volcanic rifted margins using 2D and 3D numerical forward modelling, seismic data, and data obtained during IODP Expedition 396 “Mid Norwegian Margin Magmatism and Paleoclimate Implications”. The research will involve:

• 2D modelling of volcanic margin formation to understand relative importance of margin structure, mantle composition, and mantle thermal anomalies on amount and distribution of magmatic addition. 
• 2D model geodynamic model prediction of composition of mantle melts and matching these with observed composition to constrain conditions of melting. 
• 3D modelling of margin formation linked to mantle melting to test how 3D margin structure controls melt distribution. 
• Integration of observational constraints from the North Atlantic volcanic margins with forward 2D and 3D dynamic models of passive margin formation to understand sedimentary basin development, topographic evolution, and nature of the distal margin along volcanic margins. 
• Collaboration with project partners at University of Oslo and University of Aarhus. 
• Presentation of results at international conferences.
• Production of written reports and publication of results in peer-reviewed geoscience journals. 

Research theme

The overall research goal of the Brenk lab is to improve methods used for structure-based drug design and to apply these methods to design inhibitors for enzymes with biological relevance. A key point in our research is the interplay of theoretical and experimental methods. More information can be found on our homepage https://www.uib.no/en/rg/brenk/98283/research-brenk-lab).

The advertised position will be attached to the eHACS project (Escaping the Combinatorial Explosion: Expert-Enhanced Heuristic Navigation of Chemical Space) which is about integrating knowledge-based expert guidance, modern molecular design, and empowering AI with the goal to develop a new De novo design method. More information about eHACS can be found here: https://www.uib.no/en/rg/brenk/152446/escaping-combinatorial-explosion-expert-enhanced-heuristic-navigation-chemical-space

Currently, large research efforts are ongoing to develop AI methods suitable for De novo design, scoring functions to predict the affinities of the designed compounds, and docking methods. In fact, the field is so attractive that new methods are almost published weekly. However, the validation of the AI methods is often very limited. Mostly, no activity data for AI-generated or -scored compounds is presented. If such data is included, it is typically limited to protein kinases which are considered to be easy targets due to wealth of published data. 

The core research objective of this project is to integrate machine learning and AI to bridge population level data (epidemiology) and genomics (bioinformatics) to enhance public health outcomes, with a particular focus on women’s health. The long-term goal is to develop innovative diagnostic tools to predict adverse female health outcomes by integrating extensive epidemiological and molecular data from three continents: the United States, Norway, and India. Specifically, the project will integrate diverse epidemiological data (health registry data from Norway, CDC data from the US, and NFHS data from India) with genomics data (transcriptomics, metabolomics, GWAS, protein-interaction) to identify causal relationships and common molecular pathways in women’s health-related diseases and disorders. By developing advanced machine learning methods, the goal is to create a diagnostic and early risk prediction tool that, if successful, could enhance the prevention, diagnosis, and treatment of female morbidity and mortality.

The host research group, led by Professor Helge Ræder, MD, PhD, at the Department of Clinical Science, University of Bergen, applies AI in endocrinology and digital health. Our research integrates AI, wearable and nearable sensor data, continuous glucose monitoring data, self-reported data, and multi-omics analysis to develop predictive models for steroid hormone disorders, particularly adrenal insufficiency and cortisol dysregulation. 

The project targets patients with stress-related hormone conditions, including cortisol deficiency disorders (e.g., Addison’s disease) and cortisol overproduction disorders (e.g., Cushing’s disease, metabolic syndrome, cardiovascular disease, sleep and shift work disorders). Beyond clinical applications, the research may contribute to optimizing athletic performance, eating habits, and sleep cycles. In collaboration with Stanford University’s Center for Genomics and Personalized Medicine, we aim to advance real-time monitoring solutions for endocrine health.

The postdoctoral fellow will develop AI-driven models for cortisol prediction (as well as related hormones), including in real-time, by combining physiological signals (i.e., sleep, activity, food intake, and fasting) from wearables and other sources with multi-omics biomarkers. The project involves refining deep learning approaches, such as recurrent and convolutional neural networks, to improve personalized health monitoring. Opportunities include integrating multi-omics data from iPSC-derived adrenocortical and ovarian organoids to enhance model precision. The fellow will contribute to feature engineering, model validation, and optimizing AI methodologies for clinical implementation.

• Analyze security of Machine Learning (ML) models against data modifications, using principles of differential cryptanalysis.
• Analyze security of ML models against attacks that aim to recover parameters of the model (e.g., weights of a neural network).
• Investigate whether it is possible to improve security of ML models by making them more resilient against aforesaid attacks, by using techniques from differential and linear cryptanalysis.

The researcher will work with a team at the Selmer Center in Secure Communication including, among others, Adj. Prof. Vincent Rijmen, Adj. Prof. Stjepan Picek, and Prof. Lilya Budaghyan.

The position is for a fixed term of 3 years and is associated with The Center for Digital Narrative (CDN). The Center focuses on algorithmic narrativity, interactive environments, materialities, and shifting cultural contexts in which digital narratives are received and processed. The CDN investigates the ways that the interactions of human authors and non-human agents renew narrative forms, how digital narratives have changed over time, and how textual materialities and cultural contexts are now reshaping the use and function of digital narrative. The activities of the Center include research group meetings, interactions with international research leaders in digital narrative, researcher training, PhD summer schools, frequent symposia and exhibitions.

Work task will include: 

1. 2D and 3D modelling of the effect of climate-controlled surface processes on mountain building. 

2. Inverse geodynamic modelling of mountain building applied to the Southern Alps New Zealand. 

3. Investigate 3D aspects of mountain belt formation focussing on formation of orogenic oroclines. 

The project will involve on-going collaboration with GFZ Potsdam, SCRIPPS, Barcelona University. 

The successful candidate will be expected to take a leading role in developing research in these areas and to publish results in leading, peer-reviewed publications. He/she will also be expected to participate in other activities/tasks such as mentoring of master and PhD students.

Salmon is susceptible for a range of viral diseases, and as for humans, it is challenging to develop efficient viral diseases. Farmed fish are particularly vulnerable due to high stocking density in the cages, and more effective vaccines than those currently available is needed to reduce mortality among fish and thus contribute to several of the UN’s sustainability goals, including goal no. 2 which is to eradicate hunger.

In this post-doctoral project, we will investigate adaptive immune responses in salmon after vaccination. We will vaccinate salmon with an mRNA vaccine, compare the responses with other vaccine types, isolate immune cells from various immune organs and do single cell RNA sequencing. In addition to transcriptomics, will methods such as microscopy, immunostaining and spatial transcriptomics be central, as well as work with RNA and cultivation of viruses.

The project is based on work in Gyri Haugland’s research group where we have developed protocols for making an mRNA vaccine for salmon, as well as the fact that we have carried out the first vaccine trials. The candidate gets to work in a busy and engaging research environment with other post doctors, PhDs, master’s and bachelor’s students. There will also be an opportunity for a research stay with our partner in Germany.

The Department of Archaeology, History, Cultural Studies and Religion invites applications for a postdoctoral position in the Study of Religion for a research project on strong individual commitment in ancient forms of Christianity, commencing 01.01.2026, or as agreed upon. The position is for a fixed-term period of three years.

The position is earmarked for research on ancient Christian poetry from its emergence to the 6th century CE.

This is the first of three postdoctoral positions that will be advertised connected to the RADHEART project. The successful applicant will become part of a large and dynamic research environment focused on the ancient world at the department and a closely collaborating research group that works on individual commitment in radical ancient religion. The successful applicant will get the chance to interact with top scholars in the field, attend conferences and have research stays.

• To lead work on implementation and development of frameworks and methods for equity-adjusted cost-effectiveness studies in FairChoices: DCP Analytics Tool (www.fairchocies.org), and test these for selected maternal and childhood interventions, and interventions targeting non-communicable diseases relevant for inclusion in essential service packages in low- and middle-income countries.
• To develop equity-impact modelling in the FairChoices: DCP Analytics Tool for selected maternal and childhood interventions, and non-communicable disease interventions.
• To comprehensively document frameworks and methods for quantitative equity-impact analyses in FairChoices: DCP Analytics Tool. 
• Based on this work, to publish papers in peer-reviewed international journals.