Wang lab

Principles behind gene regulation and protein-protein interactions

We always welcome motivated postdocs and graduate students to join us. Our approach is highly interdisciplinary and the technologies we exploit include genome-wide pull down, mass spectrometry, chemical biology, interpretable machine learning models in studying epigenetic regulatory rules; directed evolution, deep learning, computer modeling and simulation in protein engineering; genome editing, CRISPR screening, high throughput imaging, Hi-C, biophysical modeling, machine learning in 3D epigenomics; microfluidic instrumentation, single cell omics techniques, interpretable neural network in systems epigenomics. Our lab consists of both computational and experimental experts. We have also collaborated closely with various labs and clinicians specialized in immune cell specialization and autoimmune diseases. We welcome creative minds from any background to join us.

1. Genomics, epigenomics and 3D genome

(1) Computational positions.

We are looking for self-motivated candidates who have profound knowledge of machine learning and statistics or solid training in other quantitative sciences such as computational chemistry, physics and engineering. Proficient programming skills are essential. Previous experience of analyzing genomic data or developing deep learning methods is a plus.

(2) Experimental positions.

We are looking for self-motivated candidates who have extensive experience of developing and implementing new genomic and epigenomic technologies particularly at single cell and single molecule level, which often requires knowledge and expertise in molecular biology, chemical biology, bioengineering and cell biology.

2. Biochemistry, chemical biology, imaging and biotechnology

An ideal candidate should have solid training in biochemistry, chemical biology, physical chemistry or other related areas. We are looking for candidates who have extensive experience or are motivated to gain expertise in such as directed evolution, protein purification, SILAC mass spectrometry, immunoprecipitation, imaging, CRISPR and other related technologies. There will be a close collaboration with computational biologists in the group to integrate experimental and computational analyses.

3. Protein engineering, computational biophysics, modeling and simulation

An ideal candidate should have solid training in protein engineering, protein design, machine learning, computational chemistry/biophysics or related quantitative fields. We are looking for candidates who are highly motivated, have extensive experience with deep learning, neural network and/or other machine learning methods in analyzing big data of biomolecules, are interested to combine machine learning with computer modeling. Previous experience with molecular dynamics simulation, free energy calculation and force field development with some knowledge and strong interest in machine learning is also a good fit. Proficient programming skills are essential.

Interested applicants should send CV and statement of research to Please also arrange three letters of recommendation sent directly to the above email address.