PhD Research Fellow — 3D Genome & Cancer Genomics

Stipendiat; Lørenskog; On-site; NOK 551,000 - 600,000; Full time

Job summary

The University of Oslo invites applications for a PhD Research Fellow in bioinformatics and computational biology at the EpiGen unit, Ahus campus in Lørenskog. The three-year fellowship is free of teaching obligations and leads to a PhD within UiO.

The successful candidate will join an interdisciplinary team to develop a biophysically interpretable machine learning model that integrates TF–DNA interactions, epigenetic features, and 3D genome data to identify functional regulatory mutations in

Qualifications

• Master's degree in bioinformatics or computational biology.
• Good written and oral English proficiency.
• Documented knowledge of statistical theory and methods.
• Documented shell script, R, Python, and C programming skills.
• Documented experience in big data analysis.
• Documented experience in high-throughput sequencing data analysis.

Responsibilities

• Develop data mining methods such as network analysis in 3D genome regulation.
• Integrate transcription factor–DNA interactions, epigenetic features, and 3D genome data.
• Identify functional non-coding mutations and their targets in long-distance regions.
• Validate predictions using in vivo and in vitro data.

Skills

Molecular biology

Genomics

Data mining

Machine learning

Computational programming

Python

R

Shell scripting

C programming

Big data analysis

Hi-C data analysis

Education

Master's degree in bioinformatics or computational biology

MSc in physics, computer science, applied mathematics, or statistics (acceptable)

Tools

R

Python

Shell scripting

C programming

Job description

The University of Oslo invites applications for a PhD Research Fellow in bioinformatics and computational biology at the EpiGen unit, Ahus campus in Lørenskog. The three-year fellowship is free of teaching obligations and leads to a PhD within UiO.

The successful candidate will join an interdisciplinary team to develop a biophysically interpretable machine learning model that integrates TF–DNA interactions, epigenetic features, and 3D genome data to identify functional regulatory mutations in