Postdoc in Machine Learning for Perturbational Single Cell Analysis
School of Electrical Engineering and Computer Science at KTH
KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.
Professor Tuuli Lappalainen’s and Dr. Stefan Bauer’s research groups are looking for a postdoc for a collaborative project to develop novel computational methods to infer causal regulatory networks in human cells from large-scale perturbational experiments.
Large-scale in vitro cellular experimentation with genetic interventions (e.g. with CRISPR) and cellular readouts are opening exciting novel opportunities to infer causal mechanisms of regulatory networks of the cell. Integrated with genetic and genomic data of human diseases, this has important applications in understanding processes underlying disease pathologies and e.g. identifying drug targets.
There is great demand for new machine learning methods, such as those based on active and reinforcement learning, to extract biologically meaningful insights from these complex data. Ideally, these methods and data form a feedback loop the statistical inference further informs prioritization of maximally informative next set of experiments.
The research and project focus is thus driven by real-world challenges in biomedicine with the potential for high scientific societal impact. The required work covers a wide spectrum between theory and application, with plenty of flexibility to adjust projects to your background and preferences.
You will be involved and have the opportunity to work with two labs with state-of-the-art machine learning and human genomics and genetics expertise:
Prof Tuuli Lappalainen’s research group studies functional genetic variation in human populations. We are particularly interested in characterizing how genetic variants affect the transcriptome, and how these cellular changes contribute to genetic risk for both common and rare diseases and traits. The lab is based at the New York Genome Center in New York City, USA, and at KTH Royal Institute of Technology and SciLifeLab in Stockholm, Sweden.
Stefan Bauer is an Assistant Professor at KTH Stockholm, affiliated with the Wallenberg AI, Autonomous Systems and Software Program (WASP), and a CIFAR Azrieli Global Scholar. Using and developing tools of causality and deep learning, his research focuses on the longstanding goal of artificial intelligence to design machines that can extrapolate experience across environments and tasks.
What we offer
- A position at a leading technical university that generates knowledge and skills for a sustainable future
- Engaged and ambitious colleagues along with a creative, international and dynamic working environment
- Mentorship in both genomics and machine learning from leading experts of both fields
- A creative role that combines a high degree of independence with mentorship and close interactions with other team members
- Opportunity to develop cutting-edge statistical modeling methods to solve important biological problems, with potential for high impact discoveries
- A possibility to pursue collaborative projects locally and internationally, and develop an international network and profile as a researcher
- A supportive lab environment including mentorship for future career steps
- Works in Stockholm, in close proximity to nature
- Help to relocate and be settled in Sweden and at KTH
- A doctoral degree or an equivalent foreign degree, in Statistics, Computational biology, or a related field. This eligibility requirement must be met no later than the time the employment decision is made
- Research experience of using or developing statistical methods, with solid programming skills
- We are searching for a person with research expertise and a publication record, with a high motivation to work and excel in an interdisciplinary team
- A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
- It is considered as a great advantage with a background in Biology (where experience in Computational biology is essential) as well as Statistics (particularly Deep learning)
- We appreciate a track record showing collaborative skills, independence and teaching abilities
- Awareness of diversity and equal opportunity issues, with specific focus on gender equality
Great emphasis will be placed on personal competency.