Postdoctoral Positions in Functional Genomics, Computational Systems Biology, and Machine Learning at University of California Riverside
The Chaturvedi Lab at the University of California, Riverside is recruiting two Postdoctoral Scholars in Functional Genomics, Computational Systems Biology, and Machine Learning. The lab, led by Assistant Professor Sonali Chaturvedi, focuses on understanding how complex biological networks make decisions and how these can be reprogrammed for therapeutic impact. Research integrates single-cell functional genomics, transcriptomics, synthetic biology, and advanced computational modeling to study transcriptional dysregulation in disease and host–pathogen interactions.
One postdoctoral position centers on developing mechanistic and data-driven models of cellular decision-making using large-scale single-cell transcriptomic and functional genomics datasets. Responsibilities include inferring gene regulatory programs, analyzing multimodal single-cell data, and translating biological insights into predictive systems-level models. The other position emphasizes machine learning for biology, including building predictive models and digital twins of cells to capture state, dynamics, and responses to perturbation. Deep learning, representation learning, and probabilistic modeling will be applied to high-dimensional single-cell data, bridging biological understanding with scalable computational frameworks.
Applicants should have a PhD in computational biology, systems biology, machine learning, bioinformatics, biology, molecular biology, bioengineering, virology, applied mathematics, or a related field. Required skills include experience in single-cell transcriptomics, CRISPR screening, machine learning/statistical modeling, gene regulation, and handling large, high-dimensional datasets. Proficiency in Python or R and excellent communication skills are essential. The lab values creativity, organization, and the ability to work both independently and collaboratively in an interdisciplinary environment. Nice-to-have qualifications include experience with digital twins, deep learning, time-series modeling, viral/host–pathogen models, and a strong publication record.
Benefits include a competitive salary, comprehensive UC benefits (medical, dental, vision, retirement plans, paid time off), and a supportive work–life balance. The lab offers a collaborative, interdisciplinary, and intellectually stimulating environment with strong mentorship and opportunities for professional growth. To apply, email your updated CV to [email protected] and highlight relevant experience in computational biology, systems biology, and machine learning.
