The Project is concerned with Machine Learning and probabilistic modeling applied to proteomics experiments. Methodologically, this project will be focused on advanced unsupervised methods for computational inference such as Factor analysis, and autoencoding Deep Neural Networks, as well as the implementation of such methods on modern high-performance computers and clusters. We expect to collaborate with groups developing new experimental methods, particularly mass spectrometry-based proteomics methods, 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 a large component of quantitative science. Programming skills and language skills are required. Knowledge of biology and computational biology are regarded as advantageous qualifications.
Log into KTH's recruitment system, http://goo.gl/sVdq6w. Your application shall include the following documents:
- A cover letter, stating your research interests.
- Curriculum vitae.
- Transcripts from University/University College.
- A link to a piece of source code that you have been involved in developing.
Deadline for application: 2018-03-15