Eivind Valen
1 week ago
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PhD Research Fellow in Bioinformatics – Discovering Rare Proteins Across the smORF Landscape University of Oslo in Norway
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Oct 22, 2026
Country
Norway
University
University of Oslo

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About this position
The University of Oslo is recruiting a PhD Research Fellow in bioinformatics/computational biology for ORFeus, a Marie Skłodowska-Curie Actions Doctoral Network funded by Horizon Europe.
The PhD project, DC3: Discovering rare proteins across the smORF landscape, will analyse more than 15,000 public ribosome-profiling datasets to identify and characterise rare and non-canonical translation events. The research combines large-scale genomics, data aggregation, machine learning, and visualisation, with a focus on the dark proteome and its relevance to human disease.
The successful candidate will be employed by the University of Oslo and enrolled in its PhD programme. The position is based in the Department of Biosciences in Oslo, Norway, and the candidate will join Professor Eivind Valen's research group. The project also involves an international supervisory team and collaboration with academic and industry partners across Europe.
As part of the doctoral network, the PhD researcher will undertake a secondment of approximately three months at EIRNA Bio in Cork, Ireland, and participate in network-wide training, workshops, and conferences.
Eligibility highlights include a Master degree or equivalent in a relevant field, with a strong fit for candidates interested in bioinformatics, computational biology, genomics, and machine learning. The application deadline is 22 October 2026 at 23:00 (Europe/Oslo).
Apply through the Jobbnorge listing using the official application link provided in the vacancy announcement.
Funding details
Available
What's required
Applicants should have a Master degree or equivalent in a relevant field, with the post specifically aimed at research in biological sciences. The project is suitable for candidates with strong interests in bioinformatics, computational biology, genomics, machine learning, data aggregation, and visualization.
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