Buffalo, New York
Posted: 31-Mar-25
Location: Cold Spring Harbor , New York
Salary: Starting $78,000.00 per year
Employment Type:
Organization Type:
Required Education:
Internal Number: 1594
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Position Description
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The Koo Lab at the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory is looking to hire an ambitious and self-directed postdoctoral fellow at the intersection of machine learning and genomics. The project involves the development and application of advanced machine learning and deep learning techniques to understand the sequence-function relationships that govern gene regulation, with a particular emphasis on high-throughput functional genomics data. The goal of this work is to elucidate the cis-regulatory mechanisms that coordinate gene expression, including the role of sequence motifs and other regulatory elements, to ultimately contribute to precision medicine strategies for complex diseases such as cancer.
The ideal candidate will have a strong background in computational biology, machine learning, and bioinformatics, with a passion for understanding the underlying mechanisms of gene regulation. The position provides an exciting opportunity to work at the intersection of data science, genomics, and disease biology, with access to cutting-edge technologies and interdisciplinary collaboration within a highly dynamic and supportive research environment.
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Position Responsibilities
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- Develop and implement machine learning and deep learning models to analyze and interpret high-throughput functional genomics data, such as ChIP-seq, RNA-seq, and ATAC-seq.
- Investigate the sequence-function relationships in cis-regulatory regions, with a focus on identifying motifs and understanding their roles in gene regulation.
- Enhance model interpretability techniques to gain insights into the mechanistic underpinnings of regulatory processes.
- Collaborate with experimental biologists to validate computational predictions and integrate multi-omic data.
- Contribute to the development of tools and resources for the broader research community.
- Communicate findings through publications, presentations, and collaborations with internal and external researchers.
The Koo Lab studies the functional impact of genomic mutations through a computational lens using data-driven machine learning solutions. We are broadly interested in applications for studying gene regulation and protein (dys)function. Our approach develops methods to interpret high-performing deep learning models to distill knowledge that they learn from big, noisy biological sequence data. Our goal is to elucidate biological mechanisms that underly sequence-function relationships, with a broader aim of advancing precision medicine for complex diseases, including cancer. (For more info: http://koolab.cshl.edu/)
The Koo Lab is situated in the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory (CSHL). The Simons Center is a closely-knit community of researchers that study diverse topics in genomics, evolution, biophysics, immunology, and neuroscience. Members also benefit from easy access to CSHL’s vibrant Meetings and Courses program, which hosts leading conferences in many biological disciplines. CSHL is located on the north shore of Long Island and is readily accessible from New York City via the Long Island Railroad.
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Position Requirements
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Candidate must have (or expect to soon receive) a PhD in a quantitative field (i.e. Computational Biology, Computer Science, Bioinformatics, Biophysics, or related discipline).; possess a strong publication record with one or more first-author research publications/pre-prints in the field of quantitative biology or machine learning (broadly defined); strong written and oral communication skills; strong knowledge of Python programming; familiarity with DL frameworks (i.e. TensorFlow, PyTorch, and JAX) is a plus; familiarity with one or more of the following concepts: language models, explainable AI, robust ML, causal inference, fairness, active and lifelong learning is a plus, but not required. Prior experience in both machine learning and genomics is desirable, but expertise in one and sufficient enthusiasm to learn the other can replace lack of experience.
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Supplemental Information
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To apply: Interested candidates should email Peter Koo (koo@cshl.edu) with a cover letter, CV and contact information for two individuals who can provide letters of reference upon request. In addition, please apply via the CSHL careers website at Post Doc Computational - Koo Lab
Environment
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 and our researchers have a wide variety of backgrounds.
Compensation & Benefits
Our employees are compensated in many ways for their contributions to our mission, including competitive pay, exceptional health benefits, retirement plans, paid time off, and a range of recognition and wellness programs. Visit our CSHLBenefits,Postdoctoral Landing & Postdoctoral Fellow Stipend (Download PDF reader)(Download PDF reader)sites to learn more. The salary for this role is $78,000.00 per annum. The salary range or hourly rate listed is a good faith determination of potential base compensation that may be offered to a successful applicant for this position at the time of this job advertisement and may be modified in the future. When determining a base salary and/or rate, several factors may be considered as applicable (e.g., years of relevant experience, education, credentials, and internal equity).
CSHL is an EO 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. VEVRAA Federal Contractor
Candidate must have (or expect to soon receive) a PhD in a quantitative field (i.e. Computational Biology, Computer Science, Bioinformatics, Biophysics, or related discipline).; possess a strong publication record with one or more first-author research publications/pre-prints in the field of quantitative biology or machine learning (broadly defined); strong written and oral communication skills; strong knowledge of Python programming; familiarity with DL frameworks (i.e. TensorFlow, PyTorch, and JAX) is a plus; familiarity with one or more of the following concepts: language models, explainable AI, robust ML, causal inference, fairness, active and lifelong learning is a plus, but not required. Prior experience in both machine learning and genomics is desirable, but expertise in one and sufficient enthusiasm to learn the other can replace lack of experience.