Faculty-Non-Tenure Research Track-Dept of Bioinformatics

The Albert Einstein College of Medicine invites applications for a full-time, non-tenured research track faculty position in Bioinformatics.

We seek a PhD-level scientist with demonstrated expertise in integrating and analyzing large omic data (e.g., genomics, transcriptomics, epigenomics, microbiome) who will contribute to the research and educational missions of the new Data Science Institute.

The Data Science Institute at Einstein is a cross-disciplinary institute dedicated to advancing biomedical data science through research, education, and collaboration. The institute supports methodological innovation in statistics, bioinformatics, health research informatics, and AI/machine learning, with a focus on applications to clinical, translational, and population health research.

The successful candidate will join this growing interdisciplinary community of investigators and trainees to collaborate on developing innovative bioinformatics methodology as well as advancing scientific discovery and data science training.

Please visit the website for more information:

Albert Einstein College of Medicine Launches Data Science Institute | Montefiore Einstein

• Collaborate with faculty and research teams on the design, analysis, and interpretation of omic studies.
• Develop and apply computational and statistical approaches to address complex biomedical questions with omic data.
• Contribute to scholarly dissemination, including grant applications and peer-reviewed publications.
• Offer training to students, postdoctoral fellows, and junior investigators in bioinformatics methods.
• Participate in institutional initiatives to strengthen data science and bioinformatics research capacity.

• PhD in Bioinformatics, Computational Biology, Biostatistics, Computer Science, or a related discipline.
• Demonstrated expertise in analyzing multiple –omic data types, from either bulk tissues or single cells of human or mice.
• Strong record of scholarship, including peer-reviewed publications.
• Proficiency with bioinformatics tools and programming languages such as R and Python.
• Commitment to team science and mentorship.
• Excellent in communications.