Postdoctoral Fellow - Long-Read RNA Methods Development for Neurodegeneration

Postdoctoral Fellow - Long-Read RNA Methods Development for Neurodegeneration Organization Translational Neurogenetics Unit, Center for Alzheimer’s Disease and Related Dementias (CARD), National Institute on Aging (NIA), NIH, Framingham, MA Scientific focus area Neuroscience,RNA Biology,Genetics and Genomics A postdoctoral position is available in the Translational Neurogenetics Unit led by Dr. Kimberley Billingsley at the Center for Alzheimer's Disease and Related Dementias (CARD), National Institute on Aging, NIH. About the position 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 works by changing how genes are regulated, but for most risk loci we still don't know which transcript or protein is affected. Long-read sequencing lets us read full-length transcripts and native RNA directly, but applying these methods to postmortem brains at scale is still technically challenging. Brain tissue often has variable RNA quality, many protocols need high input, and there is a lot of room to improve how we capture isoforms, RNA modifications and cell-type information. 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. 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 approaches for generating and analysing long-read RNA data across hundreds of samples (Kouam, Mingle et al., bioRxiv 2026). The postdoctoral fellow will lead wet lab method development for long-read RNA and direct RNA sequencing in human brain. This could include optimising protocols for low-input and degraded postmortem tissue, developing targeted approaches to capture disease-relevant transcripts, improving direct RNA sequencing for studying RNA modifications, and scaling protocols through lab automation. The fellow will also help validate findings from our computational work, for example confirming novel isoforms or linking them to protein changes with the CARD proteomics team. Some computational analysis will be part of the role, and the fellow will work closely with the computational scientists in our group. 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. A PhD in molecular biology, genomics, biochemistry, neuroscience or a related field, with fewer than 5 years of postdoctoral experience. Strong hands-on experience with RNA work and next-generation sequencing library preparation is required, along with experience developing or troubleshooting lab protocols. Experience with long-read sequencing (Oxford Nanopore or PacBio), direct RNA sequencing, working with human postmortem tissue, or lab automation is a plus. Some experience with basic data analysis in R, Python or the command line would also be helpful. We are looking for someone curious and motivated who enjoys solving technical problems and working as part of a team. Disclaimer/Fine Print 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. #J-18808-Ljbffr Office of Intramural Training & Education