10 Postdoctoral Fellowship at University of Bergen, Bergen, Norway

The Faculty of Law, University of Bergen, offers a postdoctoral research fellow position affiliated to the Research Group for Climate, Energy and Environmental Law. The position is connected to the project ‘The North Sea Carbon Basin: Governance for transporting, storing, and building Carbon Capture and Storage (CCS) infrastructure in the Climate Transition’ (NorthCarb). The project is funded by Akademiaavtalen.

NorthCarb is an interdisciplinary project developing new expertise in law and comparative politics on cross-border and climate and energy policy interactions of CCS in the North Sea. It is an advantage that the person appointed has experience with research in these areas.

The position is a fixed-term contract. The duration of the position is 3 years, which may be extended to 4 years if the candidate is qualified to teach compulsory courses in the master’s program at the Faculty of Law. The successful candidate is expected to live and work in Bergen. 

This project bridges two foundational fields in computer science and mathematics: Theory of Algorithms and Extremal Combinatorics. By integrating these areas, the project seeks to develop innovative computational techniques and theoretical insights that extend the frontiers of both fields. It focuses on leveraging recent breakthroughs that combine algorithmic approaches with classical results in extremal combinatorics. The anticipated outcomes are twofold: advancing state-of-the-art parameterized and approximation algorithms to address challenging computational problems with high efficiency, and formulating new stability theorems in extremal combinatorics, driven by practical algorithmic applications.

For the position, we are looking for a scientist who will contribute to the success and visibility of the project. A successful candidate for this position will conduct research within algorithms and extremal combinatorics, as well as interacting with other members of the algorithms research group. We are looking for excellent scientists with background and experience in one or more of the following areas: graph algorithms, parameterized complexity, approximation algorithms, extremal combinatorics, structural graph theory, as well as related fields.

Are you a political scientist with an academic interest in gender and politics, and a solid background in survey research? Apply for this position at the Department of Government, University of Bergen, as a researcher on an exciting, cutting-edge ERC-funded project. We are hiring a motivated and skilled researcher for a two-year position, offering the opportunity to work at the forefront of research on political leadership, voter attitudes, and political careers, with a particular focus on gender, age, experience, elite perceptions, and the role of populist sentiments in shaping representation. 

The Department of Government, one of seven departments within the Faculty of Social Sciences, specializes in political science, studies of public policy, public administration, democracy and participation, as well as political institutions and organisation. Research at the department is structured around dedicated research groups. Applicants are encouraged to familiarize themselves with the department’s general academic profile. 

 The vision of PRO-COAST (https://www.pro-coast.eu/en/c) is to stimulate and empower local communities and civil society to support restoration and maintenance of biodiversity and ecosystem services across Europe, and thus to involve citizens and residents more actively in environmental governance. To achieve this, PRO-COAST is gathering and synthesizing knowledge to contribute to informed citizen decisions and improve participation in community decision making, at 10 coastal sites across Europe. Our goal is to avoid loss of biodiversity by empowering ecosystem managers with the knowledge to make informed decisions that conserve biodiversity in the first place. The team is led by TERO, an environmental and technology consultancy based in Greece, and funded by the EU’s Horizon Europe program.

The applicant must propose an individual project in computational narrative systems that will be the focus of the postdoctoral work. The Center for Digital Narrative’s Computational Narrative Systems node, led by Nick Montfort, studies and researches the computational modeling of narrative, which includes developing new computational models of narrative and re-implementing historical models (also known as “storytelling systems”) so that they can be easily studied and used.

The successful applicant will collaborate with researchers within the Center’s Computational Narrative Systems node and from MIT to achieve jointly authored research results. As part of the position, the applicant is also expected to re-implement historical narrative systems in Python and help determine the course of a larger collaborative CNS project dealing with such systems.

The position is connected to the Research Group for Information and Innovation Law. The research group is dedicated to investigating the legal regulation of information and innovation in modern society, as well as the technological underpinnings that support it. Our expertise encompasses a range of legal areas, including intellectual property, personal data protection and data regulation. Research group for Information and Innovation Law | University of Bergen

The advent of more sophisticated artificial intelligence is rapidly transforming the manner in which intellectual creations are conceived, produced, and disseminated. The use of AI tools has the potential to generate original content, improve innovation processes and reshape entire creative and industrial sectors. These developments challenges traditional concepts of authorship and inventorship and raise complex legal issues such as the rights to AI-assisted and AI-generated works and compensation for use of existing works in AI model training. In light of this ongoing transformation, it is imperative to undertake a critical reassessment of existing intellectual property frameworks and principles.

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.

The project will be conducted at the Computational Biology Unit (CBU, https://cbu.w.uib.no/ ), hosted within the Department of Informatics at the University of Bergen. The CBU is a leading bioinformatics research hub in Europe, encompassing 10 research groups from five diverse departments (Informatics, Biological Sciences, Chemistry, Biomedicine, and Clinical Science) in a uniquely interdisciplinary environment.
 

The project will be supervised primarily by Professor Anagha Madhusudan Joshi-Michoel, who specializes in applying machine learning and data science approaches to understand human health and disease, with a particular focus on women’s health, the project aims to contribute to this field. More about the Joshi group can be found at https://cbu.w.uib.no/joshi-group/. Co-supervisors include experts in machine learning and AI, Pekka Parviainen and Tom Michoel, alongside leading epidemiologists, Tone Bjørge and Kari Klungsøyr.

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 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 lack of validation using real-life drug discovery examples is seriously hampering the uptake of the methods in the field. Therefore, in this project we will work on validation of AI-based De novo design, scoring and docking methods using both retrospective and prospective predictions. 

For the latter, we will make use of well studied systems in the Brenk group such the enzymes FabF and NMT for which we can routinely obtain binding data and high resolution crystal structures (Espeland LO, Georgiou C, Klein R, Bhukya H, Haug BE, Underhaug J, Mainkar PS, Brenk R.

An Experimental Toolbox for Structure-Based Hit Discovery for P. aeruginosa FabF, a Promising Target for Antibiotics. ChemMedChem. 2021., Kersten C, Fleischer E, Kehrein J, Borek C, Jaenicke E, Sotriffer C, Brenk R. How To Design Selective Ligands for Highly Conserved Binding Sites: A Case Study Using N-Myristoyltransferases as a Model System. J Med Chem. 2020). The lessons learned from the validation shall also be used to develop improved methods.