A postdoctoral position in COMPUTATIONAL GENOMICS is available in Dr. Adam Siepel’s research group at the Simons Center for Quantitative Biology (SCQB), Cold Spring Harbor Laboratory.
The Siepel Group specializes in the development of probabilistic models and algorithms for inference, machine-learning methods, and applications in large-scale genomic data analysis. Of particular interest is research relevant to existing NIH-supported projects in:
1. EVOLUTIONARY GENOMICS of humans and other mammals, including inference of ancestral recombination graphs, detection of selective sweeps, comparative genomics of bats, inference of distributions of fitness effects and quantification of genetic load from linked deleterious alleles; and
2. TRANSCRIPTIONAL REGULATION in mammals, including quantification of initiation and pause-release rates from nascent RNA sequencing data, characterization of elongation rates, and evolutionary analysis of transcriptional regulation across primates and other mammals.
RELEVANT RECENT PAPERS INCLUDE THE FOLLOWING:
1. Danko CG, Choate LA, Marks BA, Rice EJ, Zhong W, …, & Siepel A. Dynamic evolution of regulatory element ensembles in primate CD4+ T-cells. Nat Ecol Evol2:537-548 (2018).
2. Huang, Y.-F. & Siepel, A. Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease. Genome Res29, 1310–1321 (2019).
3. Hejase, H. A. et al. Genomic islands of differentiation in a rapid avian radiation have been driven by recent selective sweeps. PNAS7, 202015987 (2020).
4. Hubisz, M. J., Williams, A. L. & Siepel, A. Mapping gene flow between ancient hominins through demography-aware inference of the ancestral recombination graph. PLOS Genetics16, e1008895 (2020).
5. Dukler, N., Huang, Y.-F. & Siepel, A. Phylogenetic modeling of regulatory element turnover based on epigenomic data. Mol Biol Evol37:2137-2152 (2020).
6. Blumberg, A. et al. Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data. BMC biology19, 1–17 (2021).
7. Hutton, E. R., Vakoc, C. R. & Siepel, A. ACE: a probabilistic model for characterizing gene-level essentiality in CRISPR screens. Genome Biol22, 278 (2021).
8. Hejase, H. H., Mo, Z., Campagna, L. & Siepel, A. A deep-learning approach for inference of selective sweeps from the ancestral recombination graph. Mol Biol Evol 7:msab332 (2022).
9. Dukler, N., Mughal, M. R., Ramani, R., Huang, Y.-F. & Siepel, A. Extreme purifying selection against point mutations in the human genome. Nature Communications, in press.
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Candidates should hold a Ph.D. in bioinformatics, computer science, statistics, genetics, molecular biology, applied mathematics, or a related field. Research experience (with first-author publications) in computational genomics or a closely related field. Experience with probabilistic modeling, computational statistics, and/or machine-learning. Proficiency in programming, ideally in python, R, or C/C++. Should be comfortable in a Linux environment, with large data sets and computer clusters.
Interested candidates should submit a CV, a short description of research interests and experience, and contact information for three references, by email to email@example.com. Informal inquiries are also welcome. In addition, candidates should apply online at: https://www.schooljobs.com/careers/cshl Position ID: 02744-R
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