Three multidisciplinary postdoctoral scholarships (2 years)

Umeå University is one of Sweden’s largest higher education institutions with over 37,000 students and about 4,700 employees. The University offers a diversity of high-quality education and world-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, 
which was awarded the Nobel Prize in Chemistry, was made here. At Umeå University, everything is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture. The ongoing societal transformation and large green investments in northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here. 
 
Are you interested in learning more? Read here (LINK)

The Integrated Science Lab (IceLab) (icelab.se), jointly with several departments at Umeå University, offers three postdoctoral scholarships. Applicants apply to one or more of the five proposed multidisciplinary projects, each project's PIs select their top candidate, and an IceLab-internal committee selects the three most promising project–candidate pairs for funding. 

Across the five projects, we seek candidates whose expertise falls within one or more of the following areas: computational and mathematical modeling, statistical modeling, machine learning, network science, bioinformatics, applied mathematics, biophysics, spectroscopy or microscopy, systems biology, theoretical and evolutionary biology, community ecology, ecological modeling, molecular biology, biochemistry, dynamical systems, and sports medicine or physiology. Further, postdocs should have a deep interest in scientific collaboration between researchers using theoretical and empirical approaches. 

The five projects are:  

A. Sensing the breaking point: Decoding the inputs to a cell wall integrity receptor in Chlorella vulgaris 
B. Decoding the Hidden Logic of RNA Polymerase Allocation Under Stress 
C. Adaptive immunity as an evolutionary response to unforeseen stress 
D. Multitrophic interaction networks as drivers of competitor coexistence under global change 
E. From training load to adaptation: Modeling of stress, resilience, and performance in elite female athletes 

Detailed information on and specific requirements for each project is given below. 

The IceLab Interdisciplinary Postdoctoral Program funded by Kempestiftelserna 
 
The under-explored terrain between traditional disciplines is full of fascinating and impactful research questions. At IceLab, we promote and facilitate transdisciplinary collaborations – with a focus on cutting-edge research that integrates theoretical, computational, and empirical approaches.  
 
We will welcome you to IceLab with genuine support by creative researchers working on a panel of interdisciplinary problems. You will participate in both professionally and personally rewarding and entertaining activities aimed at training a new kind of researcher. A multidisciplinary team of researchers with complementary expertise will supervise each postdoc. 
 
The two-year postdoc fellowships are financed by Kempestiftelserna and are part of the IceLab Interdisciplinary Postdoctoral Program. A fellowship amounts to 720,000 SEK over two years and 75,000 SEK for research expenses. The scholarships are tax-free.

Application deadline September 17, 2026.
Start between January and April 2027 (exact start date according to agreement). 
 
Formal qualifications 
 
To qualify as a postdoctoral scholarship holder, the postdoctoral fellow is required to have completed a doctoral degree or a foreign degree deemed equivalent to a doctoral degree. This qualification requirements must be fulfilled no later than at the time of the decision about scholarship recipient. 
 
Priority should be given to candidates who completed their doctoral degree, according to what is stipulated in the paragraph above, no later than three years prior to the decision date for the scholarship recipient. If there are special reasons, candidates who completed their doctoral degree prior to that may also be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organizations, military service, or similar circumstances, as well as clinical practice or other forms of appointment/assignment relevant to the subject area.  
 
Candidates should have experience in some of the following areas: data analytics, computational modeling, programming, mathematics, statistics, physics, molecular biology, microbiology, ecology, or systems biology – or an interest in gaining experience in these areas. Personal qualities such as collaboration, communication, strong drive and motivation, critical thinking abilities, creativity and analytical skills are essential. You should be able to take on the research independently and as part of a team. Good knowledge of oral and written English is required. 
 
Application 
A full application should include:
 
1. A cover letter clearly stating which project or projects you are particularly interested in and summarizing your qualifications, your scientific interests, and your motives for applying (max 2 pages), 
2. A curriculum vitae (CV) with publication list, 
3. Verified copy of doctoral degree certificate or documentation that clarifies when the degree of doctor is expected to be obtained, 
4. Certified copies of other diplomas, list of completed academic courses and grades, 
5. Copy of doctoral thesis,  
6. Copies of relevant publications, 
7. Contact information for at least two reference persons, 
8. Other documents that the applicant wishes to claim. 
 
The application should be written in English or Swedish, and attached documents should be in Word or PDF format. The application should be registered via Umeå University’s e-recruitment system Varbi and submitted by the deadline 17th of September 2026. 

Project descriptions, specific qualifications, and contact information 
 
(A) Sensing the breaking point: Decoding the inputs to a cell wall integrity receptor in Chlorella vulgaris

Walled cells, including microalgae, live under a constant physical tension: internal turgor pressure pushes outward, and the cell wall must contain it. Cells monitor this balance through cell wall integrity (CWI) signalling, adjusting the wall's properties when they are threatened. A central question remains unresolved in both plants and algae: what does a CWI sensor actually detect? The difficulty is that the stresses which perturb the wall change its chemistry and its mechanical stiffness at the same time, so the activating signal cannot be identified by observation alone. 
 
This project addresses that question in the green microalga Chlorella vulgaris, a commercially important production organism whose robust cell wall is simultaneously the subject of basic biological interest and a practical obstacle to extracting intracellular products. Working from an established two-laboratory collaboration and a candidate CWI receptor already identified by bioinformatic screening, the project will build a mechanistic mathematical model that couples wall chemistry, wall stiffness, and turgor pressure, then use that model to design targeted experiments that separate what the receptor detects: the chemical products of wall remodelling, the mechanical state of the wall, or a defined combination of both. The resolved model will also predict the conditions under which the wall fails, with direct relevance to controlled, low-energy cell disruption. 
 
The postdoctoral researcher will have the opportunity to: 
- Build a mechanistic model coupling wall chemistry, wall stiffness, and turgor pressure to receptor activation, using existing transcriptomic, compositional, and Brillouin microscopy data as the starting point. 
- Conduct an identifiability analysis to determine which new measurements are needed to separate confounded inputs. 
- Design and interpret experiments that decouple the chemical and mechanical inputs to the receptor. 
- Fit the model to experimental measurements acquired across decoupled conditions, attributing receptor activation to each input. 
- Use the resolved model to predict wall failure thresholds and explore their relevance to controlled cell disruption. 
- Present results and modelling problems at IceLab activities, including lunch pitch sessions and the IceLab Camp, building connections with researchers working on stress-response modelling and inference from multivariate biological data. 
 
The postdoc will be based in IceLab, hosted by the Department of Plant Physiology, and supervised by a multidisciplinary team with complementing expertise in biochemistry, biotechnology and physiology of microalgae at the Department of Chemistry. 
 
Specific Qualifications for Project A 
 
To qualify for the fellowship, the candidate should hold a PhD degree, or a foreign degree that is deemed equivalent, in computational biology, biophysics, mathematical biology, applied mathematics, or a closely related quantitative field. The ideal candidate has experience in mechanistic or mathematical modelling of biological systems, an interest in working with experimental biologists to test model predictions, and the motivation to develop their skills at the intersection of quantitative modelling, cell biology, and algal biochemistry. Experience in parameter fitting, identifiability analysis, or dynamical systems modelling is particularly relevant. Prior knowledge of cell wall biology is not required. 
 
Contact Information Project A
Laura Bacete Cano, Assistant Professor, Department of Plant Physiology, Umeå University (laura.bacete@umu.se). 
Christiane Funk. Professor, Department of Chemistry, Umeå University (Christiane.funk@umu.se).

 
(B) Decoding the Hidden Logic of RNA Polymerase Allocation Under Stress 
 
How does a single bacterial cell decide which genes to switch on when it is under attack? When bacteria face stress, such as during infection or antibiotic exposure, they must rapidly change which genes they express. At the heart of this decision is a molecular competition: a handful of regulatory proteins called sigma factors compete over a small, shared pool of the enzyme RNA polymerase, with a small RNA molecule (6S RNA) acting as a hidden referee. 6S RNA determines which sigma factor gains access to the enzyme, which dictates whether the cell keeps growing or switches into survival mode. This competition is also part of the stress responses that help bacteria resist antibiotics, making this an emerging frontier in the understanding of antimicrobial resistance. 
 
This project explores this question in Yersinia pseudotuberculosis, using a uniquely rich set of time-resolved gene-expression measurements together with mathematical and computational modeling. Because the competition for RNA polymerase cannot be observed directly, the central intellectual challenge is to reconstruct this hidden layer from the patterns it leaves in the data. The project lies at the interface of biological physics, mathematics, and microbiology. 
 
The postdoctoral researcher will have the opportunity to: 
- Develop and refine mathematical and computational models of how bacteria allocate RNA polymerase among competing regulators under stress. 
- Explore and interpret rich time-series gene-expression datasets to uncover the hidden dynamics of sigma factor competition. 
- Deepen expertise in biological and computational physics, systems biology, and microbiology, building a broader interdisciplinary scientific profile. 
- Engage with the interdisciplinary research environment at IceLab, including seminars and exchange with researchers across different disciplines. 
- Optionally gain first-hand familiarity with the experimental side of the research, for those wishing to build a profile that bridges theory and laboratory science. 
- Strengthen their independent research portfolio and scientific merits in preparation for a future research career. 
 
The postdoc will be based in IceLab, hosted by the Department of Molecular Biology, and supervised by a multidisciplinary team with complementing expertise in computational physics and systems biology. 
 
Specific Qualifications for Project B 
 
To qualify for the fellowship, the candidate should have a PhD degree, or a foreign degree deemed equivalent, in computational biology, bioinformatics, physics, systems biology, or a closely related field. The ideal candidate has a strong background in mathematical or computational modeling and data analysis, an interest in biological questions, and a desire to develop at the interface between quantitative science and microbiology. 
 
Contact Information Project B 
 
Ludvig Lizana, Professor, Department of Physics and IceLab, Umeå University (Ludvig.lizana@umu.se). 
Kemal Avican, Assistant Professor, Department of Molecular Biology and IceLab, Umeå University (Kemal.avican@umu.se). 

(C) Adaptive immunity as an evolutionary response to unforeseen stress 
 
Adaptive immunity is one of the most remarkable evolutionary innovations in vertebrates. It generates vast repertoires of receptors capable of recognizing previously unseen threats. This extraordinary ability protects organisms from rapidly evolving pathogens and contributes to long-term health through immune memory. However, these benefits come at substantial costs: adaptive immune systems require large energetic investments, extensive numbers of cells, and sophisticated mechanisms to prevent harmful self-reactivity. Despite their importance, we still do not understand why adaptive immunity evolved, why it has evolved independently multiple times, or why some species have subsequently lost it. 
 
In this project, the postdoctoral researcher will explore adaptive immunity as an anticipatory stress-response system. By combining evolutionary biology, immunology, and mathematical modeling, the project aims to identify the ecological and evolutionary conditions under which organisms benefit from investing resources in complex immune defenses. The fellowship will investigate how factors such as lifespan, body size, pathogen diversity, immune memory, and energetic constraints influence the evolution of immune strategies across the tree of life. 
 
The postdoctoral researcher will have the opportunity to: 
- Develop mathematical and computational models of adaptive immune evolution.  
- Investigate how organisms balance the costs and benefits of anticipatory defense systems.  
- Explore adaptive immunity through the perspectives of evolutionary optimization, complex systems theory, and control theory.  
- Study why immune systems differ dramatically across species and why some lineages have lost adaptive immunity.  
- Collaborate across disciplines spanning evolutionary immunology, theoretical biology, mathematics, and stress-response research.  
 
The postdoc will be based in IceLab and hosted by the Department of Molecular Biology. This project brings together researchers from the Department of Molecular Biology and the Department of Mathematics and Mathematical Statistics at Umeå University. The fellow will have the opportunity to develop expertise at the interface of biology and quantitative science. The project is particularly suitable for candidates interested in evolutionary theory, biological complexity, mathematical modeling, and the fundamental principles governing how living systems respond to uncertainty and stress. 
 
Specific Qualifications for Project C 
 
To qualify for the fellowship, the candidate should hold a PhD degree, or a foreign degree deemed equivalent, in mathematics, physics, theoretical biology, computational biology, evolutionary biology, immunology, or a related discipline. 
 
The ideal candidate has experience in mathematical or computational modeling and a strong interest in interdisciplinary research. Experience in one or more of the following areas is desirable: 
Evolutionary theory, Dynamical systems, Stochastic modeling, Control theory, Theoretical biology, Immunology or host–pathogen interactions. The candidate should be motivated to cross disciplinary boundaries and be comfortable communicating with researchers from both biological and quantitative backgrounds. 
 
Contact Information Project C 
 
Ryo Morimoto, Research Fellow, Department of Molecular Biology and MIMS, Umeå University (ryo.morimoto@umu.se).  
Eric Libby, Associate Professor, Department of Mathematics and Mathematical Statistics and IceLab, Umeå University (eric.libby@umu.se). 

(D) Multitrophic interaction networks as drivers of competitor coexistence under global change 
 
How can hundreds of plant species share the same meadow when they all compete for the same light, water, and nutrients? Classical theory says competition should leave only the strongest competitor, yet rich plant communities are everywhere. This is one of the oldest open questions in ecology, and the answer turns on something that studies of competition between species pairs miss: plants do not live in isolation. A plant's fate depends on its links to soil microbes, pollinators, and herbivores just as much as on its direct competitors. These links across different groups of organisms form networks that can flip a competitive outcome and open routes to coexistence that stay hidden when species are studied two at a time. 
 
Recent studies make this concrete. Indirect interactions through the soil between competing plant species can, on their own, keep both species in the community even when simple pairwise models predict otherwise. We now have the empirical tools to measure these interactions at high resolution and the network science methods to study the resulting multilayer structure. This project will, for the first time, build a computational framework that maps coexistence routes across all ecological layers at once, and test it against real biodiversity data from two very different systems: the Mediterranean and the Arctic. 
 
The postdoctoral researcher will have the opportunity to: 
- Explore how indirect links across several layers - competition, facilitation, soil microbes, pollinators, herbivores - shape which plant species coexist in species-rich communities. 
- Develop new higher-order network-flow models for multilayer ecological networks, extending state-of-the-art tools from network science to community-level biodiversity. 
- Learn from rich empirical datasets from two contrasting biomes: Mediterranean annual plant communities with measured competitive outcomes, soil microbiome profiles, and pollinator data; and a unique long-term Arctic herbivore exclosure experiment spanning several decades. 
- Test whether the same network mechanisms hold across biomes, and whether the breakdown of indirect links under global change predicts observed biodiversity loss. 
- Develop and share open-source software so that other research groups can apply the framework to their own community data. 
 
The postdoc will be based in IceLab, hosted by the Department of Physics, and jointly supervised by Magnus Neuman (Department of Physics) and Johan Olofsson (Department of Ecology, Environment and Geoscience). The project is at the intersection of ecology and network science and offers a strong base for the postdoc to build an independent profile across both fields. 
 
Specific Qualifications for Project D 
 
To qualify for the fellowship, the candidate should hold a PhD degree, or a foreign degree that is deemed equivalent, in a relevant field such as ecology, physics, computational science, applied mathematics, or a related discipline. 
 
The ideal candidate has a strong background in community ecology together with experience in quantitative methods, such as statistical modeling or network analysis. Documented experience with plant community ecology, field experiments, plant-soil interactions, species coexistence, or multitrophic interactions is a strong merit. Experience with computational methods such as multilayer networks, Bayesian inference, or higher-order network models is also a merit. The ability to communicate across disciplines is essential, since this project joins ecology with network science. 
 
Contact Information Project D 
 
Magnus Neuman, Staff Scientist, Department of Physics and IceLab, Umeå University (magnus.neuman@umu.se). 
Johan Olofsson, Professor, Department of Ecology, Environment and Geoscience, Umeå University (johan.olofsson@umu.se). 

(E) From training load to adaptation: Modeling of stress, resilience, and performance in elite female athletes 
 
Why do some individuals thrive under extreme stress while others succumb? This project targets a central challenge in modern biology: predicting how complex living systems respond, adapt, or fail under sustained stress. Despite major advances, we still lack the ability to identify when systems approach critical thresholds and transition into maladaptive states. Elite female endurance athletes provide a uniquely powerful human model here, operating close to the limits of physiological and psychological resilience. 
 
The project builds on a unique, large-scale longitudinal dataset following 86 Swedish elite cross-country skiers and biathletes over six years, from adolescence to international performance. The dataset integrates molecular profiles (steroidomics, mineralomics), physiology, training load, dietary patterns, psychological health, and performance outcomes, capturing human adaptation as a high-dimensional system evolving over time. Combined with biobanked blood samples enabling further molecular analyses, this creates a unique real-world experimental system. 
 
The central aim is to identify multivariate stress-response fingerprints, detect early-warning signals of instability, and uncover tipping points in human adaptation. By integrating statistical learning, dynamical systems modelling, and high-dimensional and functional longitudinal analyses, the project will develop predictive models to identify maladaptive trajectories and critical transition points, enabling timely interventions to prevent injuries, illness, and performance decline. Beyond its applied relevance, the project establishes a testbed for systems biology in which real-world human data enable the study of resilience, adaptation, and failure in complex living systems under sustained stress. 
 
The postdoctoral researcher will have the opportunity to: 
- Explore a unique, high-dimensional longitudinal dataset rarely available in human research, integrating molecular, physiological, and psychological data. 
- Develop predictive models of human adaptation, resilience, and failure under sustained stress using advanced statistical and computational methods. 
- Identify early-warning signals and tipping points in complex biological systems, uncovering stress-response fingerprints. 
- Translate complex, multi-scale data into biological insight and contribute to a predictive, systems-level understanding of human performance and health. 
- Extend the project through targeted biochemical analyses of biobanked samples, driven by emerging hypotheses and discoveries. 
 
This postdoc will be based in IceLab, hosted by the Department of Community Medicine and Rehabilitation, and supervised by a multidisciplinary team with complementing expertise in sports medicine, systems biology, psychology, and mathematical statistics. 
 
Specific Qualifications for Project E 
 
To qualify for the fellowship, the candidate should hold a PhD in a relevant field such as statistics, data science, computational biology, physics, or a related quantitative discipline. 
The ideal candidate has:
- Strong skills in statistical modelling, machine learning, or computational methods. 
- Experience working with high-dimensional, functional, and/or longitudinal data. 
- Interest in complex systems, resilience, and human biology under stress. 
- An interest in human performance and sports science (desirable but not required). 
- Ability to translate complex data into biological insight and predictive models. 
- A strong motivation for interdisciplinary, data-driven research. 
- Experience in molecular biology or biochemistry (desirable but not required). 
 
Contact Information Project E 

Michael Svensson, Associate Professor in Sports Medicine, Department of Community Medicine and Rehabilitation, Umeå University (michael.svensson@umu.se). 
Sara Sjöstedt de Luna, Professor in Mathematical Statistics, Department of Mathematics and Mathematical Statistics, Umeå University (sara.sjostedt.de.luna@umu.se).