PhD student machine learning of gene isoform variants in immune cells

Job description

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.

• Study the effect of (rare) genetic variants on isoform expression using long-read single-cell expression data from immune cells (blood and gut).
• Develop and apply computational methods to improve isoform quantification in short-read data, leveraging long-read data as a reference.
• Employ AI-based models to analyze genetic effects on isoform expression and apply these to predict effects in rare disease cases.
• Present your findings in scientific publications and at (inter) national meetings.

You will study the impact of (rare) genetic variants on isoform expression within patient cohorts. You will work with unique datasets available through the single-cell eQTLGen consortium and will benefit from international collaborations within this network.

• A (soon to be completed) MSc degree in bioinformatics, statistical genetics, computer science, artificial intelligence, molecular biology, or a related field.
• Strong communication skills and proficiency in English.
• Motivation for collaborative work and a proactive approach.
• Fundamental programming skills (e.g., Python, R, Java, Bash).
• Interest or experience in AI/machine learning and data analysis.
• Affinity with statistics or willingness to learn.
• Experience with single-cell data, long-read sequencing, or genetics is a plus but not required.