Postdoctoral Associate - Immunology
- University of Pittsburgh
- Location: Pittsburgh, PA
- Job Number: 7112365 (Ref #: pt10069)
- Posting Date: Oct 1, 2022
- Application Deadline: Open Until Filled
Qualifications or Expectations:
- Candidates must have a PhD in a quantitative discipline. Possible disciplines include, but are not limited to, computational biology, bioinformatics, systems biology/systems immunology, bioengineering/biomedical engineering, genomics, computer science and statistics.
- Candidates with training in immunology would be preferred, but this is not essential to apply to this position.
- The ideal candidate will have training in machine learning (especially deep learning), and requisite programming skills.
- Trainees will be provided several career development opportunities including support for conference travel as well as mentorship and guidance to apply for funding/career development awards.
- Visa sponsorship, if applicable, is available. Salary will be commensurate with experience.
Duties or Responsibilities:
- 1. The first direction funded by NIAID DP2 (PI: Jishnu Das) focuses on the use of a novel 3D protein network approach to uncover immunomodulatory molecular phenotypes in HIV and influenza. There are several sub-projects that involve the use of creative feature-based ML as well as deep learning approaches to construct 3D networks as well as infer immunomodulatory molecular phenotypes. A DP2 grant is given to innovative high-reward research (https://grants.nih.gov/grants/guide/pa-files/par-20-259.html) and this provides a very exciting opportunity for the postdoc to work on transformative ideas at the interface of systems biology and infectious disease.
- 2. The second direction is funded by a NHGRI U01, a large collaborative center-grant multi-PI grant (PIs: Jishnu Das, Harinder Singh and Nidhi Sahni). Through this grant, we are one of the centers in NHGRI’s latest IGVF consortium, a massive effort spanning 25 centers across North America to characterize the impact of genomic variation on function (https://www.genome.gov/Funded-Programs-Projects/Impact-of-Genomic-Variation-on-Function-Consortium). Here, sub-projects will focus on the use of ML/deep learning techniques to assemble a dynamic gene regulatory network in primary human B cells and quantify the impact of autoimmune-disease associated GWAS variants on these networks.
For more info about this position, visit:https://www.jishnulab.org/