Postdoctoral Fellow in Long-Read Transcriptomics and Multi-Omics of Neurodegeneration

Our group uses long-read sequencing of human brain tissue to understand how genetic risk leads to molecular changes in Alzheimer's disease, Parkinson's disease and related dementias. Most genetic risk for neurodegenerative disease sits outside coding regions and works by changing how genes are regulated, but for most risk loci we still don't know which transcript or protein is affected. Our current maps of brain gene expression are largely built from short-read data, which can't resolve full-length isoforms or RNA modifications. Long-read sequencing lets us read these directly.

Our group leads the CARD Long-Read Sequencing Initiative, which has generated long-read genome, RNA and methylation data across large postmortem brain cohorts including NABEC, HBCC, ROSMAP and the NIH NeuroBioBank, with matched proteomics. Previous work from our group has shown how structural variants and allele-specific methylation affect gene expression in the brain (Meredith et al., bioRxiv 2026; Kolmogorov et al., Nature Methods 2023), and we have developed scalable methods for analysing long-read RNA data across hundreds of samples (Kouam, Mingle et al., bioRxiv 2026).

The postdoctoral fellow will lead projects combining long-read RNA sequencing, direct RNA sequencing and proteomics in human brain. This includes building isoform-resolved transcript maps and using them to reinterpret existing genetic and short-read data, studying RNA modifications in disease, identifying disease-relevant isoforms at the protein level, and linking structural variants and repeat expansions to changes in transcripts and proteins. The role is mainly computational, with the option to do some wet lab work if the candidate is interested. The fellow will work closely with computational and wet lab scientists in our group, the CARD proteomics team, and collaborators at Northeastern University, the University of Cambridge, UC Santa Cruz, Baylor College of Medicine and Oxford Nanopore Technologies. CARD has in-house Oxford Nanopore sequencing and access to NIH Biowulf and cloud computing.

We are committed to mentorship, and fellows will be supported in developing their own research direction, publishing, presenting at international meetings and applying for fellowships.

Selected publications:

• Meredith M, Daida K, Moller A, Alvarez Jerez P, ... Billingsley KJ. Haplotype-resolved long-read sequencing in hundreds of diverse brains identifies structural variant impacts on expression and allele-specific methylation. bioRxiv 2026.
• Kolmogorov M, Billingsley KJ et al. Scalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved variation and methylation. Nature Methods 2023.
• Kouam C, Mingle J et al. SALRR: Scalable Analysis of Long-Read RNA-Seq enables comprehensive transcriptome profiling in human brain. bioRxiv 2026.

Applicants should have:

• A PhD in genomics, bioinformatics, computational biology, molecular biology or a related field, with fewer than 5 years of postdoctoral experience.
• Experience analysing high-throughput sequencing data and programming in Python or R in a Linux or HPC environment is required.
• Experience with long-read sequencing (Oxford Nanopore or PacBio), transcriptomics, direct RNA sequencing or proteomics data is a plus, as is a background in neuroscience or neurodegenerative disease.

We are looking for someone curious and motivated who enjoys working with large datasets and as part of a team.

U.S. citizens and permanent residents are eligible to apply. NIH welcomes foreign nationals with the exception of individuals from this list.

Candidates are subject to a background investigation.

DHHS, NIH and NIA are Equal Opportunity Employers.

NIH provides reasonable accommodations to applicants with disabilities. If you need a reasonable accommodation during the application or hiring process, please let us know.