Postdoctoral Fellow

Overview

We are seeking a highly motivated postdoctoral fellow to join Dr. Brian Crompton’s lab in advancing understanding and treatment of pediatric cancer. Dr. Brian Crompton’s lab is an interdisciplinary team with both computational and wet bench scientists that utilize omics (e.g. genomic, epigenomic, fragmentomic, proteomic) approaches to profile tumor biopsies, circulating tumor DNA, and circulating tumor cells. These efforts enable the discovery of both novel and established disease markers, which can subsequently be leveraged for improving sensitivity of tumor detection in liquid biopsies, understanding mechanisms of treatment response and resistance, and identifying clinically actionable therapeutic targets. This work is supported by access to well-annotated patient samples obtained through an extensive network of clinical investigators running multi-institutional clinical trials.

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute is a leader in life changing breakthroughs in cancer research and patient care. We are united in our mission of conquering cancer, HIV/AIDS, and related diseases. We strive to create an inclusive, diverse, and equitable environment where we provide compassionate and comprehensive care to patients of all backgrounds, and design programs to promote public health particularly among high-risk and underserved populations. We conduct groundbreaking research that advances treatment, we educate tomorrow's physician/researchers, and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.

The qualified candidate will analyze multi-omics liquid biopsy data for minimal residual disease (MRD) detection, quantification, and assessment. This project will involve applying and evaluating statistical and machine learning models for data integration and interpretation. A strong foundation in statistical modeling will be essential for applications, such as estimating detection limits, characterizing longitudinal disease dynamics, and assessing associations with clinical outcomes. These analyses have the potential to be informative for early recurrence monitoring, adjuvant therapy decisions, and risk stratification, ultimately improving pediatric patient survival and quality of life.

Key Responsibilities

• Integrating high quality multimodal data, including genomic, epigenomic, and fragmentomic data, from patient liquid biopsy samples
• Design and evaluate deep learning models for MRD detection and characterization
• Collaborate with multidisciplinary teams across Dana-Farber Cancer Institute, the Broad Institute, and more
• Mentor and guide junior staff and students

Qualifications

• Ph.D. in bioinformatics, computational biology, statistics, data science, machine learning, or a related field
• Strong background in deep learning and statistical analysis
• Proficiency in Python, R, and deep learning frameworks (e.g. PyTorch, TensorFlow)
• Strong written and verbal communication skills
• Ability to work independently and within a team environment

Preferred Qualifications

• Familiarity with pediatric oncology, liquid biopsy, or cfDNA analysis
• Knowledge of methylation, genomics, or fragmentomics
• Experience with cloud computing

At Dana-Farber Cancer Institute, we work every day to create an innovative, caring, and inclusive environment where every patient, family, and staff member feels they belong. As relentless as we are in our mission to reduce the burden of cancer for all, we are committed to having faculty and staff who offer multifaceted experiences. Cancer knows no boundaries and when it comes to hiring the most dedicated and compassionate professionals, neither do we. If working in this kind of organization inspires you, we encourage you to apply.

Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other characteristics protected by law.

Pay Transparency Statement

The hiring range is based on market pay structures, with individual salaries determined by factors such as business needs, market conditions, internal equity, and based on the candidate’s relevant experience, skills and qualifications.

For union positions, the pay range is determined by the Collective Bargaining Agreement (CBA).