We are entering a new era in genomics with exciting opportunities for computation-driven discovery. Our aim is to explore the new possibilities of what computation can do for biomedical science, from understanding sequence-based regulations to the evolution of genomes and their impact to diseases.
Lab news (2025/5): Congratulations to Yuchen for the manuscript “Inverse Flow and Consistency Models” accepted at ICML!
The epitome of life’s complexity is encoded in the simple form of genome sequence. We focus on decoding the regulatory programs encoded in the genomic sequence. With diverse genomic datasets and machine learning approaches, especially deep learning, we work on deciphering connections between sequence, chromatin organization, genome 3D architecture, gene expression, and phenotypes including diseases. We develop methods to predict, understand, and design genomic sequences.
The entanglement of sequence, function, and evolution shapes all genomes at both macro and micro scales. We develop computational methods to retrace the evolutionary history of biological circuits, combining data from model- and non-model organisms. We are especially interested in understanding the impact of evolutionary fine-tuning or remodeling of regulatory circuits on human health.
We believe in the instrumental value of machine learning, statistics, and AI method research for computational biology and other data-intensive natural sciences. We are interested in topics including deep learning, probabilistic graphical models, Bayesian inference including variational inference and MCMC, generative modeling, optimization, causal inference, and reinforcement learning. We are also interested in building softwares to enable rapid prototyping of research project-tailored machine learning models and enable automated statistical inference for robustness and reproducibility.
Please contact me at [email protected] if you are interested.
If you are interested in joining our lab as a graduate student, you should apply through any of the graduate programs at UTSW, including the computational biology track under the BME graduate program my group in your application.
We will help students develop a strong analytical mindset and knowledge for conducting cutting-edge research with computational approaches.
We are looking for postdoctoral fellows to work at the intersection of genomics and AI. We offer a competitive salary starting at $70,000 with a $20,000 bonus for anyone who successfully obtains a competitive fellowship award.
Ideal candidates should have Ph.D. or equivalent degrees in Computational Biology, Computer Science, Statistics, or a related field at the expected start time. Prior research experience in any areas including regulatory genomics, statistical or evolutionary genetics, single-cell genomics, computational structural biology, machine learning, or statistics is a plus but not required. This is a full research position, but teaching opportunities can be provided if desired. Fully remote position is possible. If interested, please email your CV, a brief description of your previous works, and your future research interests to [email protected].
We are recruiting data scientists who are interested in working in an academic research environment and developing skills in the emerging field of biomedical data science. The responsibility of the data scientist is to support our research activities through innovations in all steps of our research. We provide a variety of projects and candidate will work on one of the following direction: web development & data visualization, distributed deep learning, machine learning algorithm development, and computational biology. Ideal candidates should have a background in computer science or statistics. Please email your CV to [email protected] if interested.
We welcome motivated undergraduates to join our team. We are happy to train undergraduates in many aspects of computational biology and data science. Please contact Jian to discuss research opportunities.
The Zhou Lab is located in the Lyda Hill Department of Bioinformatics on UT Southwestern’s South Campus. Lab members also have access to considerable computational resources, including state-of-the-art GPU computing server clusters to support our deep learning research.
UT Southwestern Medical Center is an Affirmative Action/Equal Opportunity Employer. Women, minorities, veterans and individuals with disabilities are encouraged to apply.
