Postdoctoral Fellow - Computational Immunology & Translational Oncology

Postdoctoral Fellow
- Computational Immunology & Translational Oncology

Title
- Postdoctoral Fellow
- Computational Immunology & Translational Oncology

School
- Wyss Institute for Biologically Inspired Engineering

Position Description
- About the Wyss:
The Wyss Institute's mission is to transform healthcare and the environment by emulating the way nature builds. We harness the imagination of academia and the focus of industry to translate ground-breaking technologies into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don't fund because they view it as too risky. We prefer to use the word "challenging," and we love challenges.

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About this role:

The Wyss Institute for Biologically Inspired Engineering at Harvard University seeks outstanding postdoctoral applicants with expertise in immunology and computational biology, specifically bulk/single cell sequencing and AI/ML workflows, to join an interdisciplinary team of biologists and engineers to leverage in vitro organ-on-a-chip (Organ Chip) microfluidic culture systems and injectable formulations to study and model adaptive immunity in female reproductive tract cancers.

The position is within a team lead by Dr. Don Ingber and Dr. Girija Goyal which is developing model systems and AI tools to help pharma and regulatory authorities make better decisions and therapies to treat these diseases. In addition to contributing to or leading the publication of research articles, this role provides the opportunity to present your research to regulatory agencies, investors, pharma companies etc.

This role may also provide the opportunity to join a spin-out company as part of the founding team. Learn more about the innovative work here.

What you'll do:
Integrate multi-omics, spatial pathology, and clinical datasets to identify biomarkers of therapy responsiveness, define cancer patient subpopulations most likely to benefit from therapy, and support biomarker-informed clinical translation. Develop, use, and evolve computational pipelines, including for:
Analysis of bulk and single cell RNA-sequencing, spatial transcriptomics, and proteomics datasets Integration of experimental model data with public and clinical datasets Statistical modeling and survival analysis Machine learning and retrieval-augmented AI models for biomarker prioritization and decision support Work closely with cross-functional team members to develop hypotheses, interpret data, and advance project goals Work closely with the organ chip bench team to assist and/or independently design, develop, and execute in vitro experimental protocols;

generate robust and consistent data of high quality with clear scientific documentation Present key results to project teams and stakeholders. Contribute to grants, publications, and patent applications. Mentor students and technicians and provide project feedback.

Basic Qualifications
- What you'll Need:
Applicants are expected to hold a Ph.D. Experience with computer programming, such as R and/or Python is required. Able to work independently, meet specific goals and milestones, but also serve as part of a collaborative interdisciplinary team.

Additional Qualifications
- Desirable

Skills:

Experience analyzing and integrating multi-omics datasets, including single-cell RNA-seq, bulk RNA-seq, spatial transcriptomics, proteomics, secretome/exosome profiling, and functional assay data. Prior research or work experience with cancer immunology, tumor microenvironment analysis, immune cell-state annotation, pathway enrichment, and biomarker discovery, especially in the context of immunotherapy response or tertiary lymphoid structures. Experience applying machine learning and statistical modeling to biological or clinical datasets, including unsupervised clustering, dimensionality reduction, survival modeling, feature selection, and supervised learning approaches such as random forests, gradient boosting, or Cox regression.

Interest in applying large language models, retrieval-augmented generation, or AI-assisted decision-support systems to synthesize complex biomedical datasets and support expert-guided patient prioritization. Hands-on experience in performing primary human- and cancer- cell culture, complex cell-based assay development and screening technologies (including multicolor flow cytometry, RNA-seq, library preparation for scRNA-seq, MSD, ELISA, etc.) Track record of innovative research in an academic or research setting.

Exceptional organizational, technical writing abilities, and record-keeping skills. Excellent written and verbal communication skills.

Special Instructions
- Applications should contain: A complete resume. Cover letter describing research interests and goals. Full list of publications, and copies of up to three relevant scientific papers. A PDF or Word Document that contains names and…