We are seeking a Postdoctoral Fellow with a strong background in computational biology and bioinformatics to join our multidisciplinary team focused on advancing the understanding of leukemias, including acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and myeloproliferative neoplasms (MPNs). The ideal candidate will have expertise in single-cell biology, proteomics, and spatial analysis approaches.
The goal of the research is to integrate cutting-edge computational biology, single-cell or bulk transcriptomics/epigenomics, spatial transcriptomics (ex, Visium, Xenium), proteomics (ex, Lunaphore, Opal), proteomics, and functional genomics (ex, CRISPR). Our reverse translational work entails the utilization of patient primary samples and preclinical models to uncover intrinsic and extrinsic mechanisms driving leukemogenesis, therapeutic resistance, and disease progression. Applicants with machine learning and AI experience are encouraged to apply.

Key Responsibilities include:

• Analyze high-dimensional data from single-cell RNA sequencing, spatial transcriptomics, and proteomics to uncover mechanisms of leukemogenesis and resistance.
• Develop and apply computational pipelines for integrative analysis of multi-omics datasets derived from patient samples and preclinical models.
• Collaborate on functional studies to validate computational predictions using advanced spatial and molecular techniques.
• Contribute to the design and execution of projects investigating the tumor microenvironment and cell-cell interactions in leukemia.
• Present findings in internal lab meetings, national/international conferences, and publish in high-impact journals.
• Mentor junior researchers and graduate students involved in related projects.

We offer access to a unique and extensive dataset of single-cell and spatial omics from leukemia patients, providing an opportunity to delve into cutting-edge research. Our computational and experimental resources are complemented by collaborations with leading experts in the field, fostering innovation and discovery. You will join a vibrant and collaborative research environment that supports career development and training, empowering you to grow as a scientist. This position provides the chance to work on clinically relevant challenges and contribute to translational discoveries that have the potential to impact patient outcomes.

ELIGIBILITY REQUIREMENTS

• Ph.D. (or equivalent) in Computational Biology, Bioinformatics, Genomics, Cancer Biology, Immunology, or a related field, with a publication record in these areas.
• Strong background in computational biology with demonstrated experience in analyzing high-dimensional datasets, including single-cell and/or spatial data.
• Proficiency in programming languages such as Python, R, or similar.
• Knowledge of biological principles and a strong interest in cancer and immune biology, particularly leukemia.
• Experience with machine learning or advanced statistical approaches is a plus.
• Excellent written and verbal communication skills and a track record of publishing in peer-reviewed journals.
• Ability to work collaboratively in a team-oriented environment.
• While not a requirement, bioinformatics-trained individuals with additional experimental laboratory experience are encouraged to apply.

• Analyze high-dimensional data from single-cell RNA sequencing, spatial transcriptomics, and proteomics to uncover mechanisms of leukemogenesis and resistance.
• Develop and apply computational pipelines for integrative analysis of multi-omics datasets derived from patient samples and preclinical models.
• Collaborate on functional studies to validate computational predictions using advanced spatial and molecular techniques.
• Contribute to the design and execution of projects investigating the tumor microenvironment and cell-cell interactions in leukemia.
• Present findings in internal lab meetings, national/international conferences, and publish in high-impact journals.
• Mentor junior researchers and graduate students involved in related projects