Doctoral student in Computational Biology and Machine Learning

KTH -- Royal Institute of Technology
Science for Life Laboratory


This project is concerned with the Machine Learning and probabilistic modeling of spacial multi-omics data, particularly from Parkinson's disease samples. Methodologically, this project will be focused on methods for computational predictions and inferences, as well as implementation of such methods on modern high performance computers and clusters. We collaborate with groups developing new experimental omics methods, particularly mass spectrometry-based metabolomics, spacial transcriptomics and spacial proteomics, at SciLifeLab as well as with international collaborators. This is a four-year time-limited position that can be extended up to a year with the inclusion of a maximum of 20% departmental duties, usually teaching. In order to be employed, you must apply and be accepted as a doctoral student at KTH. The starting date is open for discussion, though ideally we would like the successful candidate to start as soon as possible.


A suitable background for this position would be a master's degree in Computer Science, Physics, Statistics or any other discipline with large component of quantitative science. Programming skills and language skills are required. Knowledge of biology and computational biology are regarded as advantageous qualifications.

Start date

As soon as possible

How to Apply

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Applications must include the following elements:

CV including your relevant professional experience and knowledge.
Application letter with a brief description of why you want to pursue research studies, about what your academic interests are and how they relate to your previous studies and future goals. (Maximum 2 pages long)
Copies of diplomas and grades from previous university studies and certificates of fulfilled language requirements (see above). Translations into English or Swedish if the original document is not issued in one of these languages.Copies of originals must be certified.
Representative publications or technical reports. For longer documents, please provide a summary (abstract) and a web link to the full text.