KTH - Royal Institute of Technology
Stockholm, Sweden
Job description
The Project is concerned with Machine Learning and probabilistic modeling applied to cancer, in particular somatic evolution in cancer and cancer heterogeneity. Methodologically, this project will be focused on advanced methods for computational inference such as Expectation Maximization, Sequential Markov Chain, Markov Chain Monte Carlo (MCMC), and Particle MCMC methods, as well as implementation of such methods on modern high performance computers and clusters. We expect to collaborate with Swedish groups developing new experimental methods and data at SciLifeLab as well as with international collaborators, e.g., Shah Lab for Computational Cancer Biology at the British Colombia Cancer Research Center.
Qualifications
A suitable background for this position would be a PhD in Engineering physics, Technical biology, Computer science or similar with qualifications in machine learning and algorithms. An interest in biology is a merit. The applicant must have a high proficiency in spoken and written English. Applicants must be strongly motivated for postdoctoral studies, possess the ability to work independently and perform critical analysis and also possess good levels of cooperative and communicative abilities.