Postdoctoral researcher machine learning of gene isoform variants in immune c

Postdoctoral researcher on machine learning of gene isoform variants in immune c

Deadline 23 Feb ’26 Published 10 Feb ’26 Vacancy

Gene splicing can generate multiple transcripts (isoforms) from a single gene, potentially influencing disease mechanisms. However, for many genes, the full repertoire of isoforms is still unknown and therefore knowledge on the impact of isoforms for rare diseases in clinical settings is limited. With the development of long‑read sequencing technologies, reliable identification and quantification of these transcripts is now possible. We are seeking a motivated postdoctoral researcher to investigate and predict the relationship between gene isoforms and (rare) disease.

You will study the impact of (rare) genetic variants on isoform expression using long‑read single‑cell expression data from immune cells (blood and gut). Your main tasks will include:

• Developing methodology to improve isoform quantification from short‑read data, leveraging long‑read data as a reference.
• Designing and implementing AI‑driven models to predict the effects of genetic variation on isoform expression from genome sequences.
• Prioritizing isoforms for disease‑relevant phenotypes using inferred functional data.
• Extending existing tools to move from gene to isoform function prediction.

The methods you develop will help increase the diagnostic yield for rare disease patients. Applications will include analysis of large‑scale whole genome sequencing datasets from UMCG patients and international consortia (e.g., Solve

RD, Genomics‑England, GREGoR). You will disseminate findings through high‑impact publications and presentations at international conferences. This position is part of the single‑cell eQTLGen consortium, led by the Functional Genomics group at UMCG’s Department of Genetics. You will have access to unique datasets and benefit from collaboration within an international network.

• A PhD (or are close to graduating) in machine learning, bioinformatics, statistical genetics, molecular biology, or a related field;
• Proficiency in English and good team and organizational skills;
• Strong programming and data analysis skills (e.g., Python, R, Java, Bash);
• Experience with AI or sequence‑based models (preferred);
• Experience with long‑read sequencing data (preferred);
• Affinity with statistics or willingness to learn.

• A 3‑year contract (36 hours per week).
• A starting salary depending on qualifications and experience, scale 10 (max. €5.669 gross salary based on a full‑time position).
• 8% holiday allowance, 8.3% year‑end bonus.
• The conditions of employment comply with the Collective Labour Agreement for Medical Centres (CAO‑UMC).
• Access to world‑class genomic datasets and high‑performance computing infrastructure.
• Supervision by an experienced, internationally recognized research team.
• Extensive opportunities for training, attending conferences, and international collaborations.
• A stimulating, collegial and inclusive research environment.
• Excellent career prospects: our PhD graduates often continue in academia, industry, or data‑driven research roles.

Intended starting date:
April 1st 2026 or upon agreement.

Do you have questions about this position or are you unsure if your profile matches? Please contact Dr. Marc Jan Bonder (m.j.bonder). We look forward to your application!

You will be based in the Department of Genetics at the University Medical Center Groningen (UMCG) and the Faculty of Medical Sciences at the University of Groningen. The University of Groningen is a globally oriented research university ranked among the world’s top 100. The Faculty of Medical Sciences is the second oldest medical faculty in the Netherlands.

Our Functional Genomics group, supervised by Prof. Dr. Lude Franke, is part of the Genetics department and includes ~15 researchers focused on understanding disease through genetic and genomic data. The broader department consists of approximately 400 staff involved in research, diagnostics, and patient care. We value open science and active participation in major international consortia such as eQTLGen and sc-eQTLGen.