Postdoctoral Fellows - Computational Biology & Machine Learning

Responsibilities

• Lead Innovative research.
  • Conceive and execute computational research projects.
  • Develop novel algorithms and analytical frameworks to interrogate large-scale, multidimensional omics datasets.
  • Translate findings into clinically meaningful insights.
• Build Artificial Intelligence (AI)/Machine Learning (ML) tools.
  • Design, implement, document, and publicly release AI/ML models - including deep learning approaches - for integrative analysis of cancer genomic data.
  • Contribute resources that advance the broader scientific community.
• Engineer scalable pipelines.
  • Develop and maintain robust, reproducible computational pipelines for processing, integrating, and managing complex biomedical datasets across multiple data modalities.
• Drive scientific communication.
  • Lead and contribute to the preparation of high-impact scientific manuscripts, grant and fellowship applications, and conference presentations.
  • Represent the lab at national and international scientific meetings.
• Collaborate across disciplines.
  • Actively contribute to team meetings and foster a culture of scientific excellence within a diverse, interdisciplinary research environment.

Qualifications

• A PhD in Bioinformatics, Computational Biology, Systems Biology, Quantitative Genomics, Biomedical Engineering, Machine Learning, Computer Science (with a computational biology focus), or a closely related field is required.
• Candidates at all stages of their postdoctoral career (0–5 years of postdoctoral experience) are encouraged to apply.
• Strong foundation in statistical and computational modeling and data analysis applied to genomics questions is required.
• Experience with Artificial Intelligence (AI)/Machine Learning (ML) (deep learning) methods applied to cancer genomics is considered a strong asset.
• Demonstrated experience developing or applying computational or statistical pipelines to molecular, biological, clinical, or multi-omics data.
• Proficiency in Python, R, and/or C/C++, with hands-on experience using scientific computing libraries (e.g., pandas, NumPy, SciPy, scikit-learn, Bioconductor).
• Familiarity with cloud or high-performance computing (HPC) environments, such as Google Cloud, Amazon AWS, SLURM/SGE-based clusters, or equivalent infrastructure.
• Experience applying AI/ML and deep learning methods to cancer genomics problems - particularly single-cell omics, spatial omics, epigenomics, or liquid biopsy fragmentomics is highly valued.
• Prior work with large-scale biomedical datasets, including multi-omics, single-cell, spatial, clinical genomics, or treatment-response data is highly valued.
• A track record of peer-reviewed publications commensurate with career stage in computational biology, bioinformatics, biomedical data science, or related fields is highly valued.
• Proven ability to collaborate effectively within large, interdisciplinary teams.
• Strong organizational skills with the ability to manage multiple priorities and meet deadlines in a fast-paced research environment.
• Excellent written and verbal communication skills in English, including demonstrated scientific writing ability.
• Ability to obtain and maintain a T1/Public Trust background check.