To apply for this position, please email Dr. Jiangbin Ye at firstname.lastname@example.org.
The Department of Radiation Oncology and Biology at Stanford University is seeking a highly motivated postdoctoral candidate to investigate how metabolic reprogramming regulates chromatin remodeling in cancer progression using metabolomic and epigenetic tools.
A common hallmark of cancer is the modulation of metabolic pathways to reprogram epigenetic landscape and block cell differentiation. Dr. Jiangbin Ye’s professional interest is to investigate the causes and consequences of the abnormal metabolic phenotypes of tumor cells, with the prospect that metabolic intervention approaches might be developed to restore these metabolic pathways to improve differentiation therapy. The lab’s current goal is (1) To explore the complex role of metabolic reprogramming in epigenetic regulations, and how cell fate and differentiation process are controlled by these epigenetic regulations. (2) To identify metabolites from patient samples as biomarkers for cancer early detection and diagnosis.
Ye’s lab is located in the Stanford University School of Medicine, with state-of-art research facilities. The multidiscipline research environment provides unique and outstanding training and collaborating opportunities. The candidate will have direct access to modern metabolomics research tools, including YSI biochemical analyzer, Seahorse XF Analyzer, hypoxia chamber and Agilent Q-TOF LC-MS. The lab is specialized in both untargeted and targeted metabolomics analysis, particularly isotope tracing technique for metabolic flux analysis. Dr. Ye is committed to mentoring and training for the candidate, providing all the support the candidate needs to reach the career goal.
1. Prior experience in LC-MS-based metabolomics research is required. Preference will be given to those who have extensive experience with Agilent Q-TOF or equivalent LC-MS models.
2. Ph.D. degree or equivalent within chemistry or biomedical research-related field. A strong record of peer-reviewed publications is necessary.
3. Applicants must be able to learn and develop new skills such as mass spectrometry methods that are required for the studies. Experience with common bioinformatic and biostatistical analysis tools is encouraged.
- A complete CV
- Names of 3 referees