Postdoctoral positions at QBI @ UCSF
The Quantitative Biology Institute (QBI) at the University of California, San Francisco, is recruiting six postdoctoral fellows in the area of computational structural biology. The selected candidates will join the QBI coronavirus Research group (QCRG), HIV Accessory & Regulatory Complexes center (HARC), Host-pathogen mapping initiative (HPMI), and/or Cancer Cell Map Initiative (CCMI). These four multi-disciplinary teams of systems and structural biologists, as well as infectious disease experts and clinicians, explore the molecular details of networks underlying pathogen virulence, host immunity, and cancer. By systematically mapping the physical and genetic interaction networks for various diseases and ultimately integrating the network, structure, and functional data within and across disease platforms, we aim to contribute to developing therapies. The postdoctoral fellows will work closely with Professors Andrej Sali and Ignacia Echeverria.
The fellows will lead computational efforts to integrate proteomics, genomics, and structural data to characterize pathogen and host-pathogen protein assemblies: (1) Design and execute methods to integrate data from different sources, including structural, biochemical, genetic, and proteomics data; (2) use integrative modeling approaches to determine the structures and structural ensembles of protein systems involved in pathogenicity and cancer; (3) build iterative modeling approaches that incorporate ligand SAR and protein-ligand interface information for discovery and optimization of ligands of viral proteins. Applicants are expected to communicate and collaborate within the interdisciplinary team environment.
The applicants must have received a Ph.D. degree in computational biology, biophysics, biochemistry, or a related field within the last two years or are about to graduate. They should also have a strong track record of developing and applying molecular modeling and data integration methods. A strong research record is essential, as demonstrated by publications in peer-reviewed scientific journals.
Required Skills:
- Experience in molecular modeling
- Familiarity with the data generation and analysis process for biophysical/biochemical techniques commonly used for structure characterization, such as X-ray crystallography, NMR, electron microscopy (EM), small-angle X-ray scattering (SAXS), hydrogen-deuterium exchange mass spectrometry (HDX-MS), and cross-linking mass-spectrometry
- Proficiency in using and developing molecular modeling software using Python and/or C++.
- A track record of effectively using quantitative approaches applied to biological problems
- Proficiency in computational methods and adherence to best practices for performing scientific research
- Manage multiple diverse and collaborative projects involving multiple laboratories
- Excellent oral and written communication skills
- High productivity and successful record of publishing in high-impact journals
Preferred skills:
- Experience in Bayesian statistics, machine learning/deep learning algorithms, and other commonly applied techniques in statistical data analysis.
- Familiarity with ligand docking approaches.
- Understanding of large-scale proteomics data
Apply: Please send an email with a cover letter, CV, and a copy of one scientific publication you co-authored to or .