3 Postdoctoral fellowships (2 years) in Modelling COVID-19, Antimicrobials, and the Inland Water Carbon Cycle

Umeå University

Umeå, , SE

IceLab at Umeå University: World-leading interdisciplinary research

Umeå University conducts internationally recognized research in several areas, including applied mathematics, microbiology, epidemiology, public health, sustainability, economics, and artificial intelligence.

The under-explored terrain between traditional disciplines is full of fascinating and impactful research questions. Many researchers at Umeå University strive to explore this terrain by bridging disciplinary boundaries, especially those researchers who are associated with interdisciplinary research environments such as IceLab.

At IceLab, we promote and facilitate transdisciplinary collaborations – with a focus on cutting-edge research that integrates theoretical, computational, and empirical work. We combine mathematical and computational modeling expertise with a deep interest in working with empirical researchers.

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. Each fellowship amounts to 315 000 SEK per year plus 50 000 SEK in running costs. Start September 1 – December 31, 2020 (exact start date according to agreement).

Project descriptions

Project 1: Modelling strategies for long-term suppression of COVID-19 in Sweden

The novel Corona Pandemic is holding the world in a strong grip, leading to health and economic repercussions. The Swedish COVID-19 situation has recently stabilized, but as preliminary studies show low prevalence of antibodies, the risk of resurgence will likely be significant until a vaccine is available. While virus testing, isolation of sick individuals, physical distancing and lockdowns have proven effective in controlling the pandemic, they often have substantial consequences for the economy. It is therefore of vital importance to identify suppression strategies that can be sustained over a longer time without impeding the economy. 

To suppress the COVID-19 pandemic in the longer-term, we need to learn how we can best exploit the nature and weak spots of the novel Coronavirus in order to defeat it. This project aspires to generate new insights of underlying mechanisms, determinants, patterns and the importance of superspreading in the transmission of COVID-19 through epidemiological modelling. The ultimate goal is to use these insights for developing new strategies enabling suppression of the virus, while avoiding harmful effects to the economy.

The postdoc will be placed in IceLab, hosted by the Department of Public Health and Clinical Medicine, and supervised by an interdisciplinary team of researchers with expertise in epidemiology, public health, and computational modelling.

Project 2: New targets for future antimicrobials

Bacterial infections coupled with increases in antibiotic resistance are an emerging global threat. Chronic infections contribute to this development. These infections are often treated with long-term regimens that add to antibiotic overuse. Existing antibiotics act on a limited number of bacterial pathways, and expanding the set of bacterial factors that can be targeted is therefore urgently desired. One attractive strategy is to target gene products essential for pathogens to persist at their infection site, where target inactivation prevents bacteria from staying in the niche. For such a strategy, gene products involved in bacterial stress responses allowing bacteria to adapt to new environments are of particular interest.

Using a suite of systems biology and machine learning techniques, this postdoc project aims at identifying bacterial determinants that can be explored as targets for new antimicrobials by revealing bacterial mechanisms and/or determinants that are critical for maintenance of infection in humans. Transcriptome data of clinically emerging bacteria obtained from patients with severe infections will be combined with data from experimentally validated in vitro gene expression analyses of various human pathogens exposed to infection under relevant environmental conditions.

This postdoc will be placed in IceLab, hosted by the Department of Molecular Biology, and supervised by a multidisciplinary team with complementing expertise in molecular infection biology, clinical infection biology, systems biology, and machine learning.

Project 3: Climate impact on the inland water carbon cycle

Inland waters (lakes, streams, rivers) play an important role in the global carbon cycle by emitting carbon to the atmosphere and burying carbon in sediments. Still, fundamental knowledge gaps exist because inland waters are generally studied in isolation, ignoring that carbon fluxes of inland waters are intimately linked in larger hydrological networks. This implies that current knowledge cannot adequately represent the fact that changes in one process or system trigger changes in other processes and systems, through a series of complex interactions.

This postdoc project will assess climate impacts on carbon emission, burial and export from whole networks of inland waters. You will compile empirical data along climate gradients, and use these data together with existing and your own developed models to project carbon cycling in inland waters with changing climate conditions (temperature, precipitation/runoff) and how the response depends on the configuration of the inland water networks. The research will be carried out in close collaboration with other members of the project.

This postdoc will be placed in IceLab, hosted by the Department of Ecology and Environmental Science, and supervised by an interdisciplinary team of researchers with expertise in biogeochemistry and ecology of aquatic ecosystems, network analysis, and computational modelling.


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, computer science, physics, bioinformatics, biostatistics, mathematical statistics, biology, epidemiology (relevant to project 1), limnology, geoscience, ecology, or biogeochemistry (relevant to project 3). Other relevant fields will be considered. Preference should be given to those who were awarded a PhD no more than three years before the application deadline. A candidate who has been awarded a degree at an earlier date may be considered if special circumstances exist. Special circumstances include absence due to illness, parental leave or clinical practice, appointments of trust in trade unions or similar circumstances. The ideal candidate will have strong skills in building and implementing mathematical and statistical models.

The applicant needs additionally to have excellent skills in modern computer programming languages such as C++, Python, or R. Personal qualities such as collaboration, communication, strong work ethics, critical thinking abilities, creativity and analytical skills are essential. You should be able to perform research independently and as part of a team. Good knowledge of oral and written English is required.

In addition, candidates particularly interested in project 2 should also be passionate about using statistical methods and machine learning algorithms to answer biological questions. Work experience of large-scale sequence analysis is a requirement.

For project 3, you should have in depth knowledge of the processes controlling carbon cycling in inland waters and research experience of modelling of carbon cycling in inland waters, preferably in both lakes and streams/rivers.


A full application should include:

  1. A cover letter summarizing your qualifications, your scientific interests, which project or projects you are particularly interested in, 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 2.1.6-1394-20 for project 1, FS 2.1.6-1395-20 for project 2, and FS 2.1.6-1396-20 for project 3, both in the file name and in the subject field of the email, to [email protected]. The application can be written in English or Swedish. A copy of the application should also be sent to [email protected]. Application deadline is 31 August 2020.

Further Information

For more information, contact Professor Joacim Rocklöv, [email protected], about project 1; Professor Maria Fällman, [email protected], about project 2; and Professor Jan Karlsson, [email protected], about project 3.

We look forward to receiving your application.