Position Description:

An NIH-funded postdoctoral position is available in the Computational and Systems Biology research group within the Biostatistics & Computational Biology Branch (BCBB) at the National Institute of Environmental Health Sciences (NIEHS), located in Research Triangle Park, North Carolina. The position will be mentored by Dr. Benedict Anchang (https://www.niehs.nih.gov/research/atniehs/labs/bcb/staff/anchang). A central research focus of our laboratory is the development of causal artificial intelligence (AI), machine learning, and systems biology methods for understanding how genetic, pharmacological, and environmental perturbations rewire molecular networks and alter cellular states. We develop computational and statistical frameworks that combine causal discovery, probabilistic graphical models, network inference, and modern AI approaches to reconstruct gene regulatory circuits, identify causal drivers of disease, predict cellular responses to perturbations, and generate interpretable models of biological systems. We also maintain strong collaborations with national and international investigators across computational, experimental, and clinical research.

The successful candidate will develop and apply advanced computational, statistical, and AI/ML methods to analyze large-scale perturbation datasets generated using CRISPR-based functional genomics technologies, including Perturb-seq, CRISPRi, CRISPR knockout, pooled genetic screens, and pharmacological or environmental perturbation experiments. Research will focus on interpretable causal inference for reconstructing gene regulatory networks, signaling pathways, and cellular state transitions from single-cell perturbation data using probabilistic graphical models, mechanistic systems biology models, causal discovery algorithms, graph neural networks, and perturbation-based frameworks such as Nested Effects Models (NEMs).

Large-scale multimodal datasets serve as the primary biological testbed for developing and validating these computational methods. Research integrates genomic, transcriptomic, epigenomic, proteomic, metabolomic, single-cell, spatial transcriptomic, spatial proteomic, and spatial metabolomic data together with public reference resources, including the Human Cell Atlas and other national and international consortium datasets. These methods are applied to address important biomedical questions in environmental health, tissue injury and repair, inflammation, immune regulation, kidney disease, reproductive biology, cancer, drug discovery, and precision medicine. Current projects include characterizing spatial molecular responses following ischemic kidney injury and investigating how genetic variation and environmental exposures influence cellular responses, immune dynamics, and disease susceptibility across diverse biological systems.

Qualifications: Prospective candidates should have completed, or be close to completing, a Ph.D. in bioinformatics, computational biology, biostatistics, statistics, computer science, systems biology, genetics, applied mathematics, machine learning, artificial intelligence, biomedical engineering, or a closely related quantitative life science discipline. Applicants with backgrounds in causal AI, graph machine learning, probabilistic graphical models, reinforcement learning for biology, or computational network biology are encouraged to apply.

Required qualifications include: Strong programming skills and experience working with large-scale biological datasets; Proficiency in one or more of the following: R, Python, MATLAB, Linux/Unix, Shell scripting, and/or Java; Strong background in statistical learning, machine learning, computational biology, or systems biology. Experience analyzing next-generation sequencing datasets, including bulk and/or single-cell genomics data. Familiarity with statistical modeling, probabilistic methods, or network analysis.

Preferred qualifications: Experience with single-cell RNA sequencing, Perturb-seq, CRISPR screening, functional genomics, or spatial omics technologies, Experience developing computational methods for causal inference, gene regulatory network reconstruction, signaling network modeling, probabilistic graphical models, mechanistic systems biology modeling, Bayesian modeling, or machine learning. Familiarity with multi-omics data integration, spatial biology, graph-based machine learning, deep learning, or generative models. Experience with high-performance computing, cloud computing, or scalable analysis of large biological datasets. Interest in developing computational methods that integrate mechanistic biological knowledge with modern AI approaches to generate interpretable and predictive models of disease.

Excellent written and verbal communication skills in English are essential.

Research environment: The successful candidate will work in a highly collaborative and interdisciplinary environment alongside computational scientists, statisticians, clinicians, and experimental biologists at NIEHS, NCI, and collaborating institutions. The position provides opportunities to develop novel computational methodologies while collaborating closely with experimental laboratories generating cutting-edge CRISPR perturbation, single-cell, and spatial multi-omics datasets. Candidates will be expected to develop independent research directions, lead methodological innovations, publish in leading computational biology and biomedical journals, contribute to open-source software, and collaborate closely with experimental investigators across NIH and partner institutions.

Application Instruction: Interested candidates should submit their curriculum vitae, a detailed statement of their research interests, and the names and contact information for three references to Dr. Anchang Benedict at benedict.anchang@nih.gov.

Position Location: Research Triangle Park, NC, USA

Application Deadline Date: Open until filled