PhD Student
PhD fellowship to develop machine learning / deep learning approaches in Computational Biology & Gene Regulation Full-time
Job description
A funded PhD candidate position is available in the Computational Biology & Gene Regulation group led by Anthony Mathelier at the Centre for Molecular Medicine Norway (NCMM), University of Oslo, Nordic EMBL partner for Molecular Medicine. See mathelierlab.com for further information. The position is part of the recently funded project “Cis-regulatory signatures for improved identification and stratification of breast cancer subtypes” selected through the Rosa sløyfe 2020 – Personalized breast cancer treatment call by the Norwegian Cancer Society. The position will start in the first half of 2021 and is funded for three (3) years with possibility for extension.
The project aims at providing a map of active regulatory regions in breast cancer patients and developing machine-learning approaches to better stratify patients and identify breast cancer subtype cis-regulatory signatures. The selected candidate will specifically be involved in the implementation of a machine-learning approach to co-optimize the clusterization of patients and regulatory regions and will develop deep learning models to decipher the gene regulatory networks active in the identified cis-regulatory signatures. The developed methods will be applied to large experimental data sets publicly available as well as generated in house.
We seek a highly motivated individual with documented experience with machine learning / deep learning models development ideally applied to high-throughput genomics data. We are looking for applicants excited about combining life sciences and computer science to analyze gene expression regulation. The successful candidate will be collaborative, independent, with strong enthusiasm for research, and should have experience in programming (mainly Python, R, and bash) dedicated to the analysis of large-scale genomics data. Being familiar with gene expression regulation in general, transcription factor binding, and the analysis of transcriptomics data (e.g. CAGE) analysis is an advantage. The position is open to applicants with a Master degree in computational biology/bioinformatics, computer science, or related fields. We offer a stimulating environment with excellent working and social benefits.
Qualification requirements
- Master degree in computational biology, bioinformatics, biostatistics, or a related field
- Proficiency in programming (Python, R, bash)
- Documented experience with machine learning / deep learning method development
- Ability to collaborate with researchers from different fields and at different career stages
- Willingness to be part of a team to share knowledge and skills
- Ability to communicate science
- Knowledge of eukaryotic gene expression regulation
- Knowledge of molecular biology
- Experience with analysis of genomics data sets
- High drive for science
- Proficiency in English
- Knowledge of CAGE (Cap Analysis of Gene Expression) data analysis is an advantage
We offer
The PhD candidate position will be placed as SKO 1017 (position code) with salary NOK 482 200 – 535 200 per year depending on qualifications.
attractive welfare benefits and a generous pension agreement, in addition to Oslo’s family-friendly environment with its rich opportunities for culture and outdoor activities
How to apply
Applicants should include (1) a cover letter outlining motivations, career goals, past achievements, and research interests, (2) a CV with list of publications, and (3) three referees contact information. The application with attachments (pdf) must be delivered in our electronic recruiting system, please follow the link “apply for this job” at https://www.jobbnorge.no/en/available-jobs/job/197793/doctoral-research-fellowship-in-computational-biology-gene-regulation. Foreign applicants are advised to attach an explanation of their University’s grading system. Please note that all documents should be in English.
Applicants may be called in for an interview.