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.
2020-08-31 Postdoctoral fellowship application deadline
2021-01-01 Project start deadline