An NIH-funded postdoctoral position in Bioinformatics and Computational Systems Biology: Causal Discovery and AI for Single-Cell Perturbation and Multi-Omics 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. Under the mentorship of Dr. Benedict Anchang, the successful candidate will contribute to the development of next-generation computational, statistical, and artificial intelligence (AI) methods for causal discovery, network inference, and predictive modeling of complex biological systems. The laboratory integrates large-scale perturbation datasets, single-cell and spatial multi-omics technologies, and public reference resources, including the Human Cell Atlas and other consortium datasets, to uncover the molecular mechanisms through which genetic, pharmacological, and environmental perturbations influence cellular states, tissue organization, and disease progression. The research combines methodological innovation in causal AI with applications in environmental health, inflammation, tissue injury and repair, kidney disease, reproductive biology, cancer, and precision medicine through close collaborations with computational, experimental, and clinical investigators across NIH and partner institutions.
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). 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.
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.
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. Benedict Anchang