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Three quantitative multidisciplinary postdoctoral fellowships (2 years)

Umeå University is one of Sweden’s largest institutions of higher education with over 36,000 students and 4,100 faculty and staff. We are characterised by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison. Umeå University is also the site of the pioneering discovery of the CRISPR-Cas9 genetic scissors - a revolution in genetic engineering that has been awarded the 2020 Nobel Prize in Chemistry. We welcome your application!

The Integrated Science Lab (IceLab) ( jointly with several departments at Umeå University and SLU offer three postdoctoral scholarships that will be affiliated with one of six possible multidisciplinary projects.

The ideal postdocs will have mathematical and computational modeling expertise and a deep interest in working with empirical researchers. 

The six projects are:

  • A. Semantic analysis of single-cell data
  • B. Expulsion events as drivers of evolution in endosymbiotic systems
  • C. Modelling forest carbon sequestration in a dynamic world
  • D. Statistical learning for Chronosilviculture
  • E. Modeling arctic soil-pore networks undergoing environmental-induced structural changes
  • F. Intelligent wearable sensors for in-the-field individual assessment and feedback for improved movement control after knee injury

Detailed information on each project is given below.

The IceLab Multidisciplinary Postdoctoral Program

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 work.

We will welcome you to IceLab with genuine support by creative researchers working on a multitude 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 the Kempe foundations and are part of the IceLab Multidisciplinary Postdoctoral Program. A fellowship amounts to 2 years funding: 27,500 SEK per month in year one and 28,000 SEK per month in year two.  The scholarships is tax-free. Application deadline September 20, 2022. Start winter/spring 2023 (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. 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 computational and quantitative modeling. 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.


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. Certified copy of doctoral degree certificate,
  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.

Submit your application as a PDF marked with the reference number FS 2.1.7-1336-22, both in the file name and in the subject field of the email, to The application can be written in English or Swedish. Application deadline is 20 September 2022.

We look forward to receiving your application.


Project descriptions, specific qualifications and contact information

(A) Semantic analysis of single-cell data

Single-cell analysis is the current state of the art in biology - tissue samples such as cancer can be separated into individual cells, which are then measured at depth using sequencing. Enormous amounts of data are produced, enabling precise investigation of the content of the tissue. The bottleneck is now the researcher, and exploring a single dataset can take up to one year.

In this project we will use text analysis to guide the analysis. Over 500 000 articles have been published about just T cells, and using modern machine learning we will extract the biological knowledge. The researcher will be able to use the system in several ways; (1) asking why cells behave like they do, (2) ask which cells correspond to a certain behavior or (3) organize summaries by descriptions of certain biological phenomena.

The success of this outcome will greatly aid basic research. However, single-cell is increasingly used for advanced diagnostics, and being able to quickly analyze data is of essence for bringing this method to the clinic.

Our approach will use generative large-scale language models, fine-tuned over the available open access literature, curated literature-gene linkages, and other large datasets. The language model will then integrate with a Variational Autoencoder (VAE) model that captures the statistical properties of the single-cell data.

This postdoc will be housed in IceLab and hosted by the departments of computing science and molecular biology, supervised by a multidisciplinary team with complementing expertise in semantic analysis and single-cell analysis.

Specific Qualifications for Project A

To qualify for the fellowship, the candidate should have a PhD degree, or a foreign degree that is deemed equivalent, in one of the following fields: computing science or data science, mathematics, physics, bioinformatics, biostatistics, mathematical statistics. The ideal candidate has strong skills in transformers, ideally the GPT family.

The applicant needs additionally to have excellent skills in Python and be able to implement models in common machine learning frameworks (such as pytorch). A willingness to learn basic biological concepts is required. Experience of clustering and variational autoencoders is a merit, as is experience of computational linguistics. 

Contact Information Project A

Associate professor Johanna Björklund, Dept of Computing Science ( and researcher Johan Henriksson, Dept of Molecular Biology and MIMS ( 

(B) Expulsion events as drivers of evolution in endosymbiotic systems

Symbioses are prevalent across many biomes and can be critical to the biodiversity, productivity and survival of some of the most charismatic ecosystems on earth. However, sometimes these symbioses break down; this may be survivable for the individual partners but can also lead to widespread death of both host and symbiont. The evolutionary implications of these symbiotic breakdowns are debated, however, evidence is emerging that they may function in enhancing individual and community fitness via holobiont acclimation and adaptation. Here we use coral reefs as a model system but our results and approach may have broad applicability across other systems where host-symbiont interactions are dynamic including lichens, fig-wasps, and human gut microbiomes.

The disastrous consequences of coral bleaching over the last 20 years are well-known although the evolutionary significance of these bleaching events is less known. Despite the destruction bleaching causes, there is some evidence that given enough time, bleaching events may provide scope for acclimation and adaptation of the holobiont to survive in a warmer world. However, to date, while geologic data point to this process, available data on the evolutionary and adaptive processes themselves are lacking. Therefore, we cannot determine realistic trajectories of coral survival in a warmer world. This project will use symmetry methods, a novel mathematical approach, to explore the range of biological mechanisms and evolutionary scenarios that could give rise to the same observed bleaching phenomena and use this fresh approach to probe the scope for coral survival to the end of the century using bleaching as the mechanism.

We propose the development of a novel mathematical approach, based on symmetry transformations of differential equations, to investigate invariants and equivalence classes of models describing the co-evolution in endosymbiotic systems. Together with the Fellow, we will develop biophysically informed models of endosymbiotic dynamics and co-evolution and use them to explore the range of possible biological mechanisms governing these processes. We will emphasize the role and dynamics of expulsion events and apply this modelling paradigm to the problem of coral bleaching.

This postdoc will be placed in IceLab, hosted by the Department of Ecology and Environmental Sciences, the Department of Mathematics and Mathematical Sciences and also the Umeå Marine Sciences Centre. They will be supervised by a multidisciplinary team with complementing expertise in symbiosis breakdown, symmetry methods and marine ecology.

Specific Qualifications for Project B

To qualify for the fellowship, the candidate should have a PhD degree, or a foreign degree that is deemed equivalent, in one of the following fields: mathematics, bioinformatics, ecology/biology with a mathematical focus, or physics. The candidate would also benefit from a broad understanding of marine ecosystems.

The ideal candidate has strong skills in mathematical modelling and analysis, in particular using systems of differential equations, and a good understanding of the biological processes involved in symbiotic dynamics. Experience of modelling population dynamics and evolution, as well as using symmetry methods for differential equations are merits. Skill areas: building and implementing mathematical and statistical models.

The applicant also needs to have excellent skills in modern computer programming languages (e.g. C++, Python, MATLAB or R) preferably combined with a familiarity with handling genomic data.

Contact Information Project B

Professor Nick Kamenos, Dept. Ecology and Environmental Sciences / Umeå Marine Sciences Centre, and associate professor Fredrik Ohlsson, Dept. Mathematics and Mathematical Statistics,

(C) Modelling forest carbon sequestration in a dynamic world

Forests are responsible for most of the 120 Gt of carbon removed from the atmosphere by terrestrial ecosystems, they produce essential raw material, and are home to countless species. Consequently, they play a central role in compensating anthropogenic CO2 release (around 6 Gt CO2/year), and in the Swedish and global strategies to reach the goal of a fossil-free economy and Agenda 2030. Accurate estimations of carbon uptake in the forest canopy are therefore crucial for robust predictions of the global carbon cycle and climate change mitigation and for establishing sustainable policies for forest management.

The rate of photosynthesis is tightly linked to light intensity. Current terrestrial biosphere models, as well as landscape- or stand scale forest growth models commonly assume that light intensity changes gradually over a day and the seasons, and vertically within the canopy, so that canopy photosynthesis is always at a steady state. However, light conditions in a forest canopy are far more dynamic and can change within seconds depending on cloud movement and movement of the branches within the canopy. In contrast to the instant changes in light intensity, the photosynthetic machinery is relatively slow to respond. For example, upon a change from shade to full sunlight, it can take 20-40 minutes for the steady state rate to be reached. Not accounting for the natural fluctuations in light intensity and the slow response of photosynthesis can consequently lead to a cumulative overestimation of carbon uptake, that can be as high as 30% according to some estimates.

The aim of this project is to improve upon current modelling approaches by developing a novel, dynamic model of forest canopy carbon uptake, that considers the natural fluctuations of light intensity over and within the canopy, as well as the dynamic response of photosynthesis to these light fluctuations.

The tasks of the postdoctoral fellow will be 1) to collect data on the light conditions of two pine and two spruce stands, representing two contrasting canopy structures, that are typical in the boreal region, and a gradient of canopy density; 2) to subsequently develop a mathematical model of dynamic light conditions within forest canopies that can be adapted to different canopy structures; and 3) by integrating physiological data on photosynthetic light induction of pine and spruce with the dynamic light model, to create a dynamic photosynthesis model that provides canopy carbon uptake estimations on a range of relevant time scales.

This postdoc will be placed in IceLab, hosted by the Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, and the Department of Mathematics and Mathematical Statistics, Umeå University and supervised by a multidisciplinary team with complementing expertise in forest ecophysiology and mathematical modelling of dynamic systems.

Specific Qualifications for Project C

To qualify for the fellowship, the candidate should have a PhD degree, or a foreign degree that is deemed equivalent, in one of the following fields: Applied mathematics, Mathematical Biology, Physics, Biophysics, Plant Sciences, or Theoretical Ecology. The ideal candidate has strong skills in building and implementing mathematical and statistical models, and experience in modelling dynamic systems is of advantage.

The applicant needs additionally to have excellent skills in modern computer programming languages such as C++, Python, MATLAB or R.

Contact Information Project C

Researcher Zsofia Reka Stangl, Dept of Forest Ecology and Management, SLU (, or associate professor Eric Libby, Dept of Mathematics and Mathematical Statistics (

(D) Statistical Learning for Chronosilviculture

Time, and in particular ‘right’ timing of (their) important life-processes matters to all organisms, and most possess a circa 24 h (circadian) clock, a mechanism that enables them to coordinate their internal physiology with temporal changes in environmental light and temperature. For plants, this is critical for their strategic decisions on growth and development both in the daily and seasonal time frames. To cultivate healthy plants and optimal harness yields, knowledge of circadian and seasonal growth of plants can be harnessed in the emerging field of ‘Chronoculture’.  This project combines the skills and expertise in the molecular regulation of tree growth from the Eriksson group with statistical learning and modelling of plants, specifically tree growth in the Yu group.

We have an extensive collection of established and well-studied trees with changed expression of photoreceptors and clock component genes. Data have been collected from controlled growth conditions and field trials and will form the basis of the project.

Importantly, work in the Eriksson group has found a link between the clock and wood formation and biomass, which shows the vast importance and scope for further modelling of productivity of tree growth, including conifers. The collected data combined with knowledge obtained on tree/plant growth under both natural and controlled conditions with spatiotemporal statistical modelling tools established by Prof. Yu and his group provides means to further the growth of trees and a foundation to use the knowledge on the temporal regulation of trees development, growth, and metabolism to establish Chronosilviculture.

Specifically, this project aims to develop statistical learning approaches to understand the role of the circadian clock from genes’ expression to growth, where the explainability and interpretability of our models will be the focus. We will use four main approaches:

  1. The vast amount of data with variable temporal resolution available from genomics, physiological measures and, in particular, measures of circadian regulated responses will be addressed by constructing a versatile ‘pipeline’ for image analysis to parameterise rhythmic processes.
  2. The parameters obtained above will be introduced in statistical learning and modelling the circadian clock system and growth of wildtype and trees lacking specific photoreceptor and circadian clock functions, grown under different conditions.
  3. Evaluating and updating the statistical learning models in 2) on growth data from the same genotypes under controlled conditions.
  4. Comparison of growth of trees in 2-3) with field-grown trees to clarify the environmental effect parameters significant to growth under natural conditions as well as the role and plasticity of the clock system in timing growth in the field.

This postdoc will be placed in IceLab, hosted by the Department of Plant Physiology, Umeå Plant Science Centre and Department of Mathematics and Mathematical Statistics, and supervised by a multidisciplinary team with complementing expertise in Chrono Biology, Plant Biology, and Mathematical Statistics.

Specific Qualifications for Project D

To qualify for the scholarship, the candidate should have a PhD degree or a foreign degree deemed equivalent in one of the following fields: Bioinformatics, Chrono Biology, Mathematical Statistics, Plant Biology, or a comparable field. 

The ideal candidate has strong skills in building and implementing statistical models. The applicant should have documented knowledge of bioinformatics and statistical learning for spatiotemporal data. Documented experience with plant growth models is highly meriting.

The applicant needs additionally to have excellent skills in modern computer programming languages such as C++, Python, MATLAB or R.

Contact Information Project D

Maria E. Eriksson, Senior Lecturer, the Department of Plant Physiology (, Jun Yu, Professor, Department of Mathematics and Mathematical Statistics (

(E) Modeling arctic soil-pore networks undergoing environmental-induced structural changes


Arctic ecosystems store a tremendous amount of organic matter. Unleashing this pool as greenhouse gases will likely affect the Earth's atmospheric composition and disturb the heat radiation balance causing rising temperatures. This mechanism represents a self-reinforcing cycle as higher temperatures stimulate more gas emissions. This postdoc project aims to offer a new perspective on this soil-global warming feedback loop by using mathematical modeling and applying methods from complex network theory. We wish to better understand how organic matter gets consumed, by microbes,  inside pores permeating the arctic soil and how the consumption rate changes with climate-induced rearrangements to the pore network.

Thus far, most researchers assume that the turnover rate of organic matter depends on two key factors: the type of organic matter and the temperature-dependence of the microbial reactions breaking it down. However, recent consensus highlights a third factor: the soil's spatial organization. Presumably, it limits the microbes from reaching all buried organic substrates. This barrier likely affects the turnover of deep-soil organic matter, a massive pool stored below 0.3 meters below ground. To explain deep-soil decomposition,  researchers hypothesize that geophysical and ecological processes (e.g., rainfall, plant rooting, wormholes, etc.) reshape the soil resulting in a complex fractal network of pores. These pores allow microbes to penetrate some parts of the deep-soil organic matter while leaving pockets of well-decomposable reservoirs out of reach.

This project aims to grow our understanding of how pore-network architectures regulate organic matter turnover in Arctic soils. As these networks rearrange with a changing climate, exposing yet untouched organic matter reservoirs, this project complements the prevailing view that rising temperatures leads to higher turnover rates because microbial biochemical reactions become more efficient.

Specific Qualifications for Project E

A person with a doctorate or a foreign qualification deemed equivalent to a doctorate qualifies for employment as a postdoctoral fellow. The degree shall be in one of the following fields (or comparable): earth science, mathematics, computer science, physics, biostatistics, or mathematical statistics. The ideal candidate has strong skills in building and implementing mathematical and statistical models.

We look for candidates who enjoy collaborating in interdisciplinary teams and are good at communicating science in English to researchers from different backgrounds – experimenters, theorists, and industrial designers. The candidate should have a solid drive to move their project forward independently and be able to think critically. We value expertise in Arctic ecosystems or computer programming languages, such as C++, Python, MATLAB, or R. We also merit candidates having experience with complex networks and fractals.

Contact Information Project E

Ludvig Lizana, Associate Professor, Department of Physics,

Jonatan Klaminder, Professor, Department of Ecology and Environmental Sciences,

(F) Intelligent wearable sensors for in-the-field individual assessment and feedback for improved movement control after knee injury


Today our team performs advanced measurements in special movement and brain imaging laboratories for diagnosis and evaluation of people's ability to control their movements following injury or illness. The current project concerns the follow-up of movement control in people who have suffered an anterior cruciate ligament (ACL) injury and where the risk of a new injury is high and many persons do not return to their sport due to fear of suffering a new injury. The injury can also have an impact on the brain and nervous system as shown by us and other researchers. This impact and fear can affect the movement patterns adversely and in turn can increase the risk of injury. In addition, disadvantageous movement patterns exacerbate the development of osteoarthritis which is common following ACL injury. Within this project, we want to develop measurement methods in the form of intelligent sensors that will be able to evaluate similar aspects but outside the lab, e.g., at home or on the sports field. The end goal is to be able to provide personalised feedback and training/rehabilitation that is tailored to the individual needs and would result in improved movement control. This would increase the chances of safely returning to physical activity/ elite sports with a reduced risk of additional injuries and thus increase the well-being and quality of life of these individuals.

The first phase of the project is to develop machine learning methods for diagnosing and evaluating the ability to move and the related brain activity in people with injury of the ACL, based on advanced laboratory measurements in relation to non-injured control subjects with different levels of athletic training. In a second phase, portable sensors with custom machine learning models will be made to perform the corresponding evaluations in portable solutions out-of-the-lab. In a possible third phase, we will adapt the machine learning models to embedded processors, in order to develop a solution that can be part of patients' everyday lives and enable more efficient and individual rehabilitation programs. Such solutions could most likely in the long run benefit many people with other types of movement deficits and imply a breakthrough for precision medicine for different groups, e.g., to prevent falls in elderly or fragile people.

Specific Qualifications for Project F

To qualify for the fellowship, the candidate should have a PhD degree, or a foreign degree that is deemed equivalent, in one of the following fields: computer science, electrical engineering, physics, bioinformatics, biostatistics, mathematics or a comparable field.   The applicant's degree, or through supplementary education, should have a specialization that includes, and ideally combines, computer science with an AI or machine learning focus; electrical engineering with a sensor and signal processing focus; or with a medical device specialization.  The ideal candidate has experience with implementations of advanced sensing technology and machine learning in an embedded systems context. Knowledge of simulation of human movements and advanced laboratory-based motion analysis methods and various software for this is also meritorious but not necessary.

The applicant needs additionally to have excellent skills in modern computer programming languages such as Python, C++, MATLAB, or R; as well as modern machine learning frameworks such as Pytorch, Tensorflow, or JAX.

Contact Information Project F

For more information, please contact Professor Charlotte Häger, Dept of community medicine and rehabilitation; physiotherapy (, or Professor Tomas Nordström, Dept of applied physics and electronics (