Tenure-Track Faculty in Systems & Computational Biology

Albert Einstein College of Medicine (Bronx, NY); New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

Albert Einstein College of Medicine in New York invites applications for a tenure-track faculty position in the Department of Systems and Computational Biology in collaboration with the Montefiore Einstein Comprehensive Cancer Center.

The ideal candidate will lead innovative, quantitative cancer biology research across computational, mathematical, and engineering disciplines, collaborating with clinical and experimental investigators.

Qualifications

• Demonstrated excellence in a quantitative discipline such as computational biology, systems biology, computer science, statistics, mathematics, physics, or engineering.
• Strong record of innovation in computational methods, including machine learning, AI, image analysis, multimodal integration, and/or software development.
• Ability to work collaboratively across disciplines, including with cancer biologists, clinicians, pathologists, epidemiologists, and data scientists.
• Have a clear and compelling research program with the potential to attract external funding and advance the mission of the Department and the Montefiore Einstein Comprehensive Cancer Center.

Responsibilities

• Develop and lead a rigorous research program addressing cancer biology using computational and quantitative approaches.
• Collaborate with experimental and clinical investigators to translate discoveries into patient benefit.
• Contribute to cross-disciplinary training, mentorship, and grant funding initiatives.

Skills

Quantitative discipline

Computational methods

AI/ML

Interdisciplinary collaboration

Job description

The Albert Einstein College of Medicine, one of the nation’s leading research-intensive medical schools, invites applications for a tenure-track faculty position in the Department of Systems and Computational Biology in collaboration with the Montefiore Einstein Comprehensive Cancer Center.

Our mission is to advance the understanding of living systems by developing and integrating theoretical, computational, and experimental approaches to investigate the principles governing complex cancer-related pathobiological systems. We seek exceptional scientists whose research combines methodological innovation with cancer biological insight and who are committed to collaborative, interdisciplinary research.

We welcome applications from outstanding candidates at the Assistant, Associate, or Full Professor level, commensurate with experience and accomplishments. Successful applicants will have demonstrated excellence in a quantitative discipline—including systems biology, computational biology, mathematics, physics, computer science, engineering, or a related field—and a strong record of collaboration with experimental and/or clinical investigators. Experience applying quantitative approaches to fundamental cancer biological or biomedical questions, as evidenced by publications, extramural funding, or both, is highly desirable.

We are particularly interested in candidates whose research broadly intersects with cancer biology and biomedical discovery, including, but not limited to, the following areas:

• Quantitative approaches to cancer evolution, tumor heterogeneity, metabolomics, gene regulation, cell-cell communication, cellular plasticity, exposome biology and tumor microenvironmental interactions.
• Design of innovative computational methods and scalable software for integrative analysis of genomics, single-cell transcriptomics, epigenomics, and other multi-omic datasets.
• Methods for linking molecular, cellular, and tissue-level data with epidemiologic and real-world clinical resources, including electronic medical records and related datasets.
• Development and application of machine learning, artificial intelligence, and large language model-based approaches for cancer biology and clinical data analysis.
• Computational image analysis and multimodal data integration, including spatial transcriptomics, spatial proteomics, digital pathology, multiplexed imaging, and intravital imaging.
• Translational computational research that advances functional precision oncology, cancer immunology, and data-driven discovery in human disease.

Departmental and Cancer Center strengths and fit

The Department of Systems and Computational Biology at Einstein brings together faculty whose research spans machine learning, single-cell and spatial profiling, genomic and epigenomic analysis, clinical informatics, cancer evolution, microbiome-host interactions, and computational modeling of complex biological systems. Faculty expertise includes computational and evolutionary systems biology, machine learning algorithms for complex biological traits, integration of genomic and epigenomic data, cancer systems biology, clinical informatics, and quantitative analysis of cell-state dynamics and cell-cell communication, microbiome, and systems neuroscience.

Albert Einstein College of Medicine provides a highly collaborative environment for computational and translational science, with access to rich clinical cohorts, institutional datasets, genomics resources, imaging platforms, and opportunities for cross-disciplinary partnerships. The Montefiore Einstein Comprehensive Cancer Center offers an exceptionally rich environment for collaborative cancer research, with transdisciplinary programs that connect basic, translational, clinical, and population sciences. Its strengths include tumor microenvironment and metastasis, stem cell and cancer biology, cancer therapeutics, and cancer epidemiology, prevention, and control, supported by a strong commitment to integrating bench science, physician-scientist expertise, clinical trials, and discovery-driven translational research. The Center fosters interdisciplinary collaboration across investigators working to understand cancer mechanisms, develop novel diagnostics and therapies, and accelerate the translation of discoveries into patient benefit.

We are especially interested in candidates who will complement and extend these strengths through creative work in AI-enabled biological discovery, imaging-based phenotyping, computational pathology, and integration of large multimodal datasets across scales and patient populations.

Qualifications

Successful applicants will:

• Have demonstrated excellence in a quantitative discipline such as computational biology, systems biology, computer science, statistics, mathematics, physics, or engineering.
• Show a strong record of innovation in computational methods, including machine learning, artificial intelligence, image analysis, multimodal integration, and/or software development.
• Demonstrate the ability to work collaboratively across disciplines, including with cancer biologists, clinicians, pathologists, epidemiologists, and data scientists.
• Have a clear and compelling research program with the potential to attract external funding and advance the mission of the Department and the Montefiore Einstein Comprehensive Cancer Center.

Candidates with experience in cancer genomics, single-cell and spatial technologies, imaging analytics, clinical data science, and translational multi-omic integration are especially encouraged to apply.

Professors Aviv Bergman and Julio Aguirre-Ghiso

Cancer Systems and Computational Biology Search Committee

Albert Einstein College of Medicine

Jack and Pearl Resnick Campus

1300 Morris Park Ave. Price Center

Bronx, New York 10461

E-mail Address: [e-mail]

Subject line: Cancer-SCB Faculty Search

Equal Opportunity Statement

Equal opportunity has been and will continue to be a fundamental principle at Albert Einstein College of Medicine.

All hiring/ employment decisions are based on demonstrated capabilities, skills, and qualifications. We do not tolerate discrimination based on any protected characteristic, including race, ethnic or national origin, citizenship and immigration status, color, sex/gender, pregnancy or pregnancy-related conditions, age, creed, religion, actual or perceived disability (including persons associated with such a person), arrest and/or conviction record, military or veteran status, sexual orientation, gender expression and/or identity, an individual’s genetic information, domestic violence victim status, familial status, marital status, or any other characteristic protected by applicable federal, state, or local law. We also recognize a lawful preference in employment practices for Native Americans living on or near Indian reservations in accordance with applicable law.