The Bin Chen Laboratory (http://binchenlab.org/) uses big data and artificial intelligence to discover new or better therapeutic candidates. The lab employs a systems approach to identify drug candidates based on the potency to reverse disease molecular signatures (Chen, Nature Communications, 2017). We have collected over 20,000 bulk RNA-Seq profiles and millions of single-cell RNA-Seq profiles and drug-induced gene expression profiles, and our in-house lab pipeline processes over 100 RNA-Seq samples every day. Our postdoctoral Research Associate will help translate these data into therapeutics.
The successful candidate is expected to lead or assist with any of the following projects:
- Improving and implementing the systems-approach (octad.org) to discover therapeutics for various diseases (including liver cancer, DIPG, melanoma, MODS, metastatic breast cancer or any other conditions you are passionate about)
- Discovering novel targets or therapeutics to increase the response of cancer immunotherapy
- Mining single-cell RNA-Seq data to find drugs that target tumor microenvironment or regulate cell activity
- Developing deep learning and deep reinforcement learning algorithms to design novel therapeutics
- Leveraging cell line data to identify drug biomarkers for individualized cancer therapy
The Chen Lab is located along the Medical Mile in beautiful downtown Grand Rapids. Our Secchia Center facilities are modern and comfortable. We are a welcoming team with members from all over the world. Our work depends on close collaborations with outstanding bench scientists, data scientists, and clinicians; the successful candidate will have the opportunity to form strong working relationships at Michigan State University, Van Andel Research Institute, Spectrum Health, the University of California Berkeley, the University of California San Francisco, and Stanford University.
Recent Chen Lab work has been published in Gastroenterology, Nature Communications, and Nature Reviews Gastroenterology and Hepatology, and featured in STAT, GEN, GenomeWeb, and KCBS.
Doctorate -Bioinformatics, Biostatistics, Computer Science or related field
- A doctoral degree in Bioinformatics, Biostatistics or Computer Science or related field
- Experience with machine learning/statistical learning is required.
- Experience with drug discovery is not required, but as we primarily develop open source tools from big data, strong computational/statistical knowledge is essential.
- A demonstrated history of productivity, creativity, and passion is expected.
- Excellent written communication skills are desirable
- R/Bioconductor and Python proficiency are preferred.