Postdoctoral Fellow, Transcription Regulation using Genomics and Machine Learning

About the role

We seek a Postdoctoral Fellow to investigate how transcription factors regulate gene expression programs in cancer and how disease-associated mutations in transcription factors disrupt these programs to drive malignant phenotypes. Many cancer-linked alterations affect transcription factor binding, chromatin engagement, and transcriptional output, yet the downstream regulatory mechanisms and disease consequences remain incompletely understood. This project aims to define the molecular functions of transcription factors in normal and diseased states, map how mutations alter chromatin and transcriptional regulation, and utilize machine learning to predict and test novel mutant-TF-specific functions.

Responsibilities

• Transcription Factor Mechanism Discovery
• Define how transcription factors control gene expression, chromatin accessibility, and regulatory element activity in cancer-relevant cellular contexts.
• Map transcription factor occupancy, chromatin state, and nascent transcription using GRO-seq/ PRO-seq, CUT&RUN, ATAC-seq, DNA methylation profiling.
• Integrate transcription factor binding data with enhancer activity, promoter usage, and transcriptional outputs to identify direct regulatory targets and core gene networks.
• Apply CRISPR editing to test candidate genes, regulatory elements, and pathways.
• Mutation-Driven Regulatory Analysis
• Determine how cancer-associated mutations in transcription factors alter DNA binding, cofactor recruitment, chromatin remodeling, and transcriptional control.
• Compare wild-type and mutant transcription factor function across genomic, epigenomic, and transcriptional assays to identify mutation-specific regulatory defects.
• Characterize the mutated-TF-associated co-factors, transcription factor, chromatin remodeling through protein-protein interactions.
• Dissect how these changes impact oncogenic pathways, lineage identity, cellular plasticity, and disease progression.
• AI/ML-Guided Functional Discovery
• Collaborate closely with AI/ML scientists to apply protein language models and related computational approaches to transcription factors and their disease-associated variants.
• Use these models to predict potential novel molecular functions, protein-protein interaction partners, and mutation-associated cellular phenotypes.
• Design and execute experimental strategies to test model-derived predictions in laboratory settings, using genomic, molecular, and functional assays to validate predicted mechanisms and disease-relevant consequences.
• Help establish an iterative framework in which computational predictions inform experiments, and experimental results refine downstrea

Benefits

Additional Information

Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere. The Team Our immune cell reprogramming team integrates foundational research on immunology and disease biology with AI-modeling to develop engineered cells that harness our own immune system to detect and treat early signs of age-related diseases, like cancer, Alzheimer's, and Parkinson's. These technologies will enable precise, context-dependent therapeutic responses only when and where it is needed. You can learn more about our work here . Our work brings together three powerhouse universities - Columbia University, The Rockefeller University, and Yale University - into a single collaborative technology and discovery engine. Our Vision Pursue large scientific challenges that cannot be pursued in conventional environments Enable individual investigators to pursue their riskiest and most innovative ideas Facilitate research by scientists and clinicians at our home institutions and beyond We are a team of passionate individuals powered by technology, guided by scientific research, and driven by collaboration, working toward a mission to cure or prevent all disease. The Laboratory of Immunogenomics at CZ Biohub NY ( www.mahatlab.com ) studies the non-coding regulatory genome to understand and address immune dysfunction in diseases like cancer, autoimmune disorders, and aging. We focus on enhancers-non-coding, highly cell-type-specific transcriptional regulatory elements-and their role in shaping immune responses. We develop and utilize genomic technologies, including bulk and single-cell nascent RNA sequencing, genome editing, immune engineering, and CRISPR-based functional screens in patient biopsies, organoid systems, and mouse models. Through computational analysis integrating machine learning and AI, we map enhancer-gene networks and identify disease-driving elements. Our goal is to advanc

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Company Intel

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