Postdoctoral Fellow, Bioinformatics & Cancer Genomics ML

Stipendiat; Lørenskog; On-site; NOK 595,000 - 679,000; Full time

Benefits offered by this job

Salary NOK 595000–679000 per year

Public sector pension

Job summary

University of Oslo, Ahus campus, Lørenskog, invites applications for a Postdoctoral Research Fellow in Bioinformatics and Computational Biology. The project focuses on developing a biophysically interpretable ML framework to integrate TF–DNA, epigenomic features, and 3D genome data to identify functional regulatory interactions in cancer.

The fellow will work in an interdisciplinary team, publish research findings, and contribute to open-source tools.

Qualifications

• PhD required in bioinformatics or computational biology or related field.
• Good written and oral English proficiency.
• Doctoral degree must be submitted for evaluation by deadline and public defense required.
• Experience with statistical theory, data mining, and machine learning.
• Shell scripting, R, Python, and C programming skills demonstrated.
• Big data analysis and computational tool/package development experience.
• High-throughput sequencing data analysis or computational genome regulation experience.

Responsibilities

• Develop biophysically interpretable ML methods integrating TF–DNA, epigenetics, and 3D genome data.
• Identify functional long-range gene regulatory interactions from cancer genomes.
• Collaborate with students, PhD candidates, researchers, and national/international partners.
• Contribute to high-quality publications and open-source bioinformatics tools.
• Validation of predictions on cancer genomes and contribute to software development.

Skills

Molecular biology

Genomics

Bioinformatics

Machine learning

Data mining

Programming (Python)

Programming (R)

C programming

Education

PhD in bioinformatics or computational biology

Job description

University of Oslo, Ahus campus, Lørenskog, invites applications for a Postdoctoral Research Fellow in Bioinformatics and Computational Biology. The project focuses on developing a biophysically interpretable ML framework to integrate TF–DNA, epigenomic features, and 3D genome data to identify functional regulatory interactions in cancer.

The fellow will work in an interdisciplinary team, publish research findings, and contribute to open-source tools.