Computational Post Doc – Siepel Lab

    Computational Genomics Post Doc

    Website Cold Spring Harbor Laboratory

    Job Profile: Computational Post Doc
    Requisition Number: 02744-R
    Job Status: Full Time
    Position Category: Science/Research (RSCH)
    Education Required: Graduate Degree
    Experience Required: Less than 1 year
    Location: United States (US)
    Date Posted: Posted on 17 June 2022
    Deadline: 31 October 2022

    Position Description

    A computational post doc position in 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.


    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 Evol 2:537-548 (2018).
    2. Huang, Y.-F. & Siepel, A. Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease. Genome Res 29, 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. PNAS 7, 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 Genetics 16, e1008895 (2020).
    5. Dukler, N., Huang, Y.-F. & Siepel, A. Phylogenetic modeling of regulatory element turnover based on epigenomic data. Mol Biol Evol 37:2137-2152 (2020).
    6. Blumberg, A. et al. Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data. BMC biology 19, 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 Biol 22, 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.


    Cold Spring Harbor Laboratory is a world-renowned biomedical research institution in New York. It has shaped contemporary biomedical research and is the home of eight Nobel Prize laureates. Cold Spring Harbor Laboratory provides a highly dynamic and interactive research environment and also a unique opportunity of timely exposure to advances in various biomedical research fields and of interaction with a broad range of researchers from all over the world through its renowned Meetings and Courses program.

    We believe that science is for everyone. We have had researchers with a variety of backgrounds and believe in the importance of diversity, equity, and inclusion.

    Position Requirements

    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 Informal inquiries are also welcome. In addition, candidates should apply online at: Position ID: 02744-R

    Compensation and Benefits
    Our employees are compensated from a total rewards perspective in many ways for their contributions to our mission, including competitive pay, exceptional health benefits, retirement plans, time off, and a range of recognition and wellness programs. Visit our CSHL Benefits & Postdoctoral Landing sites to learn more

    You are required to be fully vaccinated for COVID-19 as a condition to your employment at the Laboratory, except in instances where you have a qualifying medical condition or sincerely held religious belief, practice, or observance that is contrary to receiving the vaccine. You will be required to provide proof of your vaccination on your first day of employment. For those individuals, who are unable to receive a vaccine due to access issues, they will be expected to receive the vaccine upon arrival in New York.

    CSHL is an EO/AA Employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or protected veteran status.

    Applicant Documents

    Required Documents

    1. Resume or Curriculum Vitae
    2. Cover Letter

    Optional Documents

    To find more similar jobs Click Here

    To apply for this job please visit