Join us

Are you a physicist, a mathematician, a programmer or a very techy biologist? Want to see how far we model the most complex system (un)known to mankind? This lab might be the place for you! Send your CV and tell us what you might want to do. Below are the two “archetypes” we are generally looking for. Don’t worry about not knowing everything, we will teach you along the ride! It is generally best to focus one one side (dry or wet) but you will be expected to dabble on both sides. You can’t analyze data for an experiment you don’t understand, and you can’t design and execute an experiment unless you know how the data will enter a model.

If you are looking for a postdoc then we will together write an application for a fellowship. Though the budget may have space for another position in the meanwhile

Keywords: ATAC-seq, RNA-seq, mass spectrometry, single-cell, Bayesian statistics, molecular biology, cloning, tensorflow, transcription factors, lipids, viruses, bacteria, T cells, immunology, infection models, circadian rhythm, cytokines

Computational biologist – focus on dry lab

You will develop the models, based on our data and data from others. You will suggest experiments and experimental design, to obtain the data the model requires. You should have a feel for designing models and/or know how to solve them. We will in particular work with sequence analysis, transcriptomics, ATAC, proteomics, metabolomics, single-cell and CRISPR. Basic knowledge of Bayesian statistics is very welcome. These equations are ideally solved with STAN or TensorFlow. Because of these packages, and access to machine learning packages, Python is the primary choice. R, Java and C++ are however welcome as well. In terms of equations, you want to know how to solve optimisation problems (such as for MLE), possibly basic ODE, but primarily just how to deal with truly large amounts of data. Your basic linear algebra toolbox will of course be put to good use. At some point we expect you to have learned enough biology that can you ask interesting questions on your own.

Computational biologist – focus on wet lab

You will plan and perform the experiments. Ability to clone is of utmost importance, as is understanding the chemistry of frequently used sequencing based protocols (e.g. RNA-seq, ATAC-seq, ChIP-seq, and single-cell versions). If you have an interest in robotics we might be able to satisfy your appetite for some of the high-throughput experiments. We work both in vitro and in vivo, and you should be able to cope with mouse work (although we prefer in vitro and human whenever possible!). Attention to detail whenever needed is great, but expect very few routine experiments – we develop new methods whenever it makes sense, and on the fly. You will be producing viruses and in some cases also work with infection models.

You will of course take part in analysing and interpreting the data. An ability to ask biological questions, and answer them using the data, is expected. Basic R/Python skills are ideal (but most number crunching will be done already, so just subsetting and plotting will take you far).

The location – MIMS, Umeå, Sweden

Our lab is located at Umeå University, and MIMS, a part of the Nordic EMBL Partnership for Molecular Medicine. This place has long been a centre of excellence for molecular genetics and pathogen research. Our group takes advantage of this, bringing mechanistic knowledge of disease up to modern scale with high-throughput biology. Umeå is a vibrant town, largely due to the students – international as well as from all over Sweden. Some personal favourites include the Swedish cuisine (gastropubs and microbreweries), over 6 dance styles represented, one of Swedens largest ju-jutsu clubs (among other budo), indoor and outdoor climbing, winter sports, sailing club, etc … And not being Stockholm, a place to live is affordable. Oh, and don’t worry about the language. The local pub even runs their pub quiz in english, as is the case with many other social activities! For some news from Sweden, in english, try out The local.