Computational Biologist, Immune Cell Repolarization

Biohub•New York, NY

3d•$153,000 - $191,000•Hybrid

About The Position

Biohub is a large-scale initiative integrating frontier AI models, massive compute, and experimental capabilities to accelerate scientific discovery and cure disease. The Immune Cell Reprogramming team focuses on using immunology and disease biology research combined with AI modeling to develop engineered cells that harness the immune system to detect and treat early signs of age-related diseases. This collaborative effort involves Columbia University, The Rockefeller University, and Yale University. Biohub NY is seeking an experienced computational biologist with expertise in machine learning and transcriptomic data analysis to join the Immune Cell Re-Programming group. This role requires research experience, a biology background, and the ability to develop and publish innovative computational methodologies using machine learning, statistics, and multi-omics. The position involves working on projects assigned by the group leader, Dr. Aleksandar Obradovic, and collaborating with research teams across the organization. Dr. Obradovic's group analyzes transcriptional, TCR-Seq, and spatial data to understand immune resistance to checkpoint-inhibitor immunotherapies, aiming to identify synergistic combination-therapies and regulatory targets for reprogramming the immune micro-environment. The ideal candidate will have a strong track record and a commitment to interdisciplinary collaboration.

Requirements

PhD in Systems Biology, AI / Machine learning, Statistics or MS plus relevant job experience.
1-2 years of relevant biomedical science experience, demonstrating a deep understanding of cellular biology, transcription and protein signal transduction.
Experience demonstrating the ability to implement, evaluate, and create new computational methodologies that leverage machine learning, statistics, and AI for biological research and discovery.
Experience programming in R and Python.
Experience in building and evaluating machine learning and/or neural network models on biological data, with a deep understanding of feature selection, regularization, model introspection, and interpretability.
Proficiency in using and modifying probabilistic learning or deep learning models such as RNNs, GNNs, protein sequence models, or natural language processing models.
Proven track record of individual innovation, as well as a strong ability to work collaboratively.
Outstanding interpersonal and communication skills.
Demonstrated commitment to open science and alignment with the mission and values of Biohub.

Responsibilities

Contribute to a dynamic, innovative, and collaborative program that aligns with the mission of Biohub NY.
Develop, apply, and evaluate cutting-edge computational / AI methodologies using data generated from across all research groups and incorporating relevant available datasets to develop mechanistic models of tumor-immune-stromal crosstalk.
Collaborate within an interdisciplinary research environment to develop, test, and validate models.
Engage with colleagues throughout the Biohub to uphold our values of scholarly excellence, innovation, open communication, hands-on hacking, and partnership.
Communicate progress and results with colleagues inside and outside of your team.
Publish and disseminate impactful findings through preprints (medRxiv, bioRxiv) and/or software repositories (e.g., GitHub).
Work with the Biohub team to patent and license technologies resulting from your research.