Bioinformatic Specialist – Lim Lab

Primary Work Address:
Dept of Molecular Biology, Princeton, NJ,08544

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.

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:

to investigate relationships between reproductive history and disease outcomes.

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.

• 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.

• 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.

• 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…