Computational Biologist

About the Role

We are seeking a highly motivated Computational Biologist, Bioinformatician, or Postdoctoral Researcher to join our interdisciplinary team focused on single-cell 5' RNA-seq analysis and the functional interpretation of human genetic variation. Our lab is at the forefront of using high-throughput transcriptomic data to uncover cis-regulatory elements, understand promoter and enhancer activity, and map expression quantitative trait loci (eQTLs and pQTLs) across diverse human samples including cancer and immunity.

This role offers a unique opportunity to work with cutting-edge single-cell genomics datasets, collaborate with world-class experimental and clinical researchers, and contribute to building resources that connect noncoding variation to gene regulation and disease mechanisms.

Key Responsibilities

· Analyze and interpret large-scale single-cell RNA 5'-seq (e.g., CAGE) and long-reads datasets

· Develop and apply pipelines for cis-regulatory element annotation using transcriptional readouts (promoter- and enhancer-derived RNAs)

· Perform QTL mapping (e.g., eQTLs, pQTLs, and enhancer QTLs) across patient cohorts

· Integrate genetic variation with gene expression to uncover regulatory mechanisms underlying complex traits and diseases

· Collaborate closely with wet-lab scientists, clinicians, and technology developers

· Prepare and communicate results for presentations, publications, and grant applications

· Contribute to codebase development, data visualization, and reproducible research practices

Requirements

Essential Qualifications:

· PhD (or equivalent experience) in Bioinformatics, Computational Biology, Genomics, or related field

· Strong experience in RNA-seq analysis, especially single-cell or bulk transcriptomics

· Solid knowledge of human genetics, QTL mapping, and statistical genomics

· Proficiency in programming languages such as Python and/or R

· Familiarity with genome annotation, regulatory elements, and gene expression modelling

· Ability to work both independently and as part of a collaborative, interdisciplinary team

Desirable Skills:

· Experience working with 5' capture technologies, CAGE-seq, or enhancer RNAs

· Familiarity with cloud computing platforms and high-performance computing clusters

· Prior work in population-scale genomics or biobank-scale datasets

· Knowledge of chromatin accessibility, histone modifications, or transcription factor binding datasets (e.g., ATAC-seq, ChIP-seq)

Why Join Us?

· Access to cutting-edge single-cell datasets from healthy and disease samples

· Collaborate with international experts in genomics, AI, and translational research

· Contribute to impactful research that bridges noncoding DNA and human disease with diagnostics and therapeutics

· Work in a dynamic and supportive environment with opportunities for training and career development