Employer: UMass Chan Medical School – GRN Lab
Location: Massachusetts, USA
Salary: USD 62,000 – 76,000 (per NIH stipend levels)
Closing Date: 1 November 2025
Link: umassmed.edu/grnlab/postdoc-causal-inference
Position Announcement
Applications are invited for a NIH-funded postdoctoral position in the GRN Lab at UMass Chan Medical School. Methods are being developed in this laboratory to reconstruct multi-modal, condition-dependent causal networks that govern cellular behavior from large-scale single-cell datasets. Techniques from machine learning, causal inference, statistics, and algorithm design are applied, and prior biomedical training is not required.
The appointed researcher will be expected to design and implement new computational and statistical models to reverse-engineer causal networks from high-dimensional, multi-modal data. High independence, rapid idea testing, and close collaboration with an interdisciplinary team will be provided. Nature Careers
Key Responsibilities
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Accurate and scalable algorithms will be developed for inferring multi-modal, condition-dependent networks from datasets with millions of samples and tens of thousands of genetic nodes.
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Robust evaluation metrics and benchmarking pipelines will be designed.
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Biological principles and insights across molecular, cellular, and population levels will be demonstrated.
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Open-source, user-friendly software tools will be built and maintained.
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Results will be published in top peer-reviewed journals and conferences.
Qualifications
Required
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A Ph.D. (obtained or expected) in Computer Science, Physics, Applied Mathematics, Statistics, Computational Biology, Bioinformatics, Systems Biology, or a related quantitative field is required.
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Proficiency in at least one programming language (Python, Julia, R, C/C++, or Fortran) is expected.
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An interest in network science, causal inference, or system identification is necessary.
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A track record of peer-reviewed publications and the ability to work independently and collaboratively are essential.
Preferred
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Experience in network inference, causal inference, network science, dynamical systems, probabilistic modeling, or differential equations is preferred.
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Experience in computational, statistical, or machine learning method development and GPU computing frameworks is advantageous.
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Familiarity with high-dimensional data analysis or single-cell/bulk sequencing data is welcomed.
Application Process
A single PDF containing a cover letter, CV with publications, contact details of up to three references, and up to two representative publications or preprints (with a description of the applicant’s contribution) should be submitted to the email contact listed on the Research Lab. Optional supporting materials such as code samples, public repositories, or a thesis may also be included.
The position is funded for three years and may be renewed.