Postdoctoral Fellow - Computational Immunology & Translational Oncology

Harvard University; Boston (MA); On-site; USD 60,000 - 80,000; Full time

Benefits offered by this job

Access to Harvard libraries

Option for flexible work arrangements

New laboratory space

Job summary

Harvard University is seeking a Postdoctoral Fellow in Computational Immunology & Translational Oncology to join the Wyss Institute. This role involves advanced research in immunology and computational biology, supporting advancements in therapies for female reproductive cancers.

The ideal candidate will have a Ph.D. and experience with programming, specifically in R and/or Python, and will work on integrating multi-omics datasets. This position offers competitive salary and benefits.

Qualifications

• Ph.D. required.
• Experience with programming languages (R and/or Python).
• Ability to integrate multi-omics datasets.

Responsibilities

• Integrate clinical datasets to identify therapy responsiveness.
• Develop computational pipelines for data analysis.
• Collaborate with team members to interpret data.
• Mentor students and provide feedback.

Skills

Computer programming (R and/or Python)

Analyzing and integrating multi-omics datasets

Machine learning and statistical modeling

Experimental protocol design

Excellent written and verbal communication skills

Education

Ph.D. in a relevant field

Tools

Multicolor flow cytometry

RNA-seq

Single-cell library preparation

Job description

Postdoctoral Fellow - Computational Immunology & Translational Oncology

Title: Postdoctoral Fellow – Computational Immunology & Translational Oncology

School: Wyss Institute for Biologically Inspired Engineering, Harvard University

About the Wyss

The Wyss Institute is part of Harvard University and focuses on translational research that integrates engineering and biology to address complex health challenges.

About this Role

We seek a postdoctoral researcher with expertise in immunology and computational biology – particularly bulk/single‑cell sequencing and AI/ML workflows – to join a multidisciplinary team led by Dr. Don Ingber and Dr. Girija Goyal. The role involves developing model systems and AI tools to assist pharmaceutical and regulatory agencies in advancing therapies for female reproductive tract cancers. Your research will be published, presented to stakeholders, and may contribute to spin‑out initiatives.

Responsibilities

• Integrate multi-omics, spatial pathology, and clinical datasets to identify biomarkers of therapy responsiveness, define patient subpopulations, and support biomarker‑informed clinical translation.
• Develop, use, and evolve computational pipelines, including:
  • 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
• Collaborate with cross‑functional team members to develop hypotheses, interpret data, and advance project goals.
• Partner with the organ‑chip bench team to design, develop, and execute in‑vitro experimental protocols; generate robust and consistent data with clear scientific documentation.
• Present key results to project teams and stakeholders.
• Contribute to grant applications, publications, and patent applications.
• Mentor students and technicians; provide project feedback.

Qualifications

• Ph.D. required.
• Experience with computer programming (R and/or Python) is required.
• Ability to work independently and meet goals while functioning as part of a collaborative team.
• 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, particularly in the context of immunotherapy response.
• 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.
• Hands‑on experience with primary human and cancer cell culture, complex cell‑based assay development, and screening technologies (multicolor flow cytometry, RNA‑seq, single‑cell library preparation, ELISA, etc.).
• Track record of innovative research in an academic or research setting.
• Exceptional organizational, technical writing, and record‑keeping skills.
• Excellent written and verbal communication skills.

Salary and Benefits

• Position is salaried and benefits eligible. Postdoctoral fellow salary is determined by years post‑Ph.D. (see https://postdoc.hms.harvard.edu/guidelines).
• Represented by the Harvard Academic Workers (HAW) – UAW for collective bargaining and compensation matters.
• Access to Harvard’s libraries, research core facilities, the Wyss Clinical & Translational Research Core, and the Machine Shop.
• Robust administrative and operational support for office and lab needs.
• Option for flexible work arrangements, depending on Institute needs.
• Newly constructed office and laboratory space in Boston’s Fenway District, featuring a rooftop terrace, fitness center, locker room, bike storage, and proximity to restaurants and cultural attractions.

Duration

This is a one‑year term position from the date of hire, with potential for extension contingent upon performance, business needs, and continued funding.

EEO / Non‑Discrimination Commitment Statement

Harvard University is committed to equal opportunity and non‑discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes. Harvard’s equal employment opportunity policy prohibits discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law.