Part Time Bioinformatics Jobs in New Jersey (NOW HIRING)

Primary Work Address: Dept of Molecular Biology, Princeton, NJ, 08544Current HHMI Employees, click here to apply via your Workday account.

We have an opportunity to be a Part-time Bioinformatics Specialist to join Dr. Ai Ing Lim at Princeton University. The Lim Laboratory at Princeton University studies the immune system during reproduction and development. Our research combines experimental models, human studies, and high-dimensional genomic approaches to understand how pregnancy and lactation alter immune and tissue states and how maternal exposures influence offspring long-term health and disease. This will be a part-time 20 hour per week position, in person at Princeton.

Our work combines immunology, stem cell biology, host-microbiome interactions, and reproductive biology, integrating mechanistic experimental studies with insights from human populations. We use single-cell and spatial genomics, epigenomic profiling, microbiome analysis, and large-scale human datasets to uncover biological principles in reproduction and development, with the ultimate goal of improving women's and children's health.

Dr. Lim is an HHMI Freeman Hrabowski Scholar. The Lim Lab is in the Department of Molecular Biology at Princeton University and offers a highly collaborative environment with access to state-of-the-art genomics, imaging, computational, and experimental resources. Learn more about the lab at https://www.limmunity.com/

About the Role

The Lim Laboratory is seeking a motivated and collaborative Bioinformatics Specialist to contribute to the computational aspects of our research program. The position will have two major areas of focus:

Large-scale human data analysis to investigate relationships between reproductive history and disease outcomes.

Computational analysis of experimental datasets generated within the laboratory, including single-cell RNA-seq, single-cell ATAC-seq, spatial transcriptomics, epigenomic datasets, and microbiome sequencing.

The successful candidate will work closely with the PI and experimental scientists in the laboratory to develop analytical strategies, interpret complex datasets, and connect computational findings with biological questions. We are particularly interested in someone who enjoys thinking collaboratively about biology and using computational approaches to uncover new biological insights.

What we provide:

• The opportunity to work at the interface of computational biology, immunology, reproductive biology, and human health.

• Access to diverse experimental and human datasets spanning single-cell genomics, spatial biology, epigenomics, microbiome studies, and population-scale analyses.

• Close collaboration with experimental scientists, with opportunities to contribute intellectually to the development and direction of research projects.

• Opportunities to contribute to publications and scientific presentations.

• A collaborative research environment within Princeton University and the broader computational and biomedical research community.

What you'll do:

• Analyze large-scale human datasets to investigate associations between reproductive history, immune phenotypes, and disease outcomes.

• Develop and apply reproducible computational workflows for large-scale human data analysis and facilitate expansion to additional datasets and cohorts.

• Collaborate with experimental scientists to design analytical strategies for high-dimensional datasets generated in the laboratory.

• Analyze and integrate single-cell RNA-seq, scATAC-seq, and other single-cell or multi-omic datasets.

• Analyze spatial transcriptomic datasets, including platforms such as Xenium.

• Analyze epigenomic datasets, including CUT&Tag and related approaches.

• Analyze microbiome sequencing datasets and integrate microbiome features with immune and tissue phenotypes.

• Apply appropriate statistical and computational approaches to identify biologically meaningful patterns and relationships across complex datasets.

• Develop reproducible and well-documented analytical pipelines and work with trainees to enable their use and extension across projects.

• Work closely with trainees to interpret results and communicate computational findings clearly.

• Contribute intellectually to research projects, manuscripts, and presentations.

What you bring:

• Bachelor's or master's degree in bioinformatics, Computational Biology, Biostatistics, Data Science, or a related quantitative field.

• Strong programming skills in R and/or Python.

• Experience analyzing large-scale genomic, transcriptomic, or human datasets.

• Experience with one or more of the following is highly desirable: single-cell RNA-seq, scATAC-seq, spatial transcriptomics, epigenomic analysis, microbiome analysis, or large human cohort datasets.

• Strong quantitative reasoning and familiarity with statistical approaches for complex biological or human data.

• Ability to develop reproducible computational workflows and work with large datasets.

• Strong interest in biology and an ability to work collaboratively with experimental scientists.

• Excellent analytical, problem-solving, and communication skills.

Experience in every analytical area listed above is not required. We particularly value candidates with strong quantitative foundations, intellectual curiosity, and an interest in learning and applying new computational approaches to biological questions, and a shared commitment to advancing women's and children's health.

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 Benefits at HHMI site to learn more.

Hiring Pay Range

$110,480.00 - $138,100.00

Pay Type:

Annual

The posted range reflects HHMI's good faith estimate of the anticipated hiring salary range for this role at the time of posting. Actual hiring compensation is determined by a candidate's qualifications, experience, and internal equity.

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