Applications are invited for a postdoctoral position with a focus on machine learning and immune recognition at the University of Maryland Institute for Bioscience and Biotechnology Research (IBBR) located in Rockville, MD. Developing and employing cutting-edge algorithms to accurately model and engineer proteins of therapeutic and medical interest, we are located in a state-of-the-art facility in Rockville, Maryland with outstanding collaborators in structural biology, immunology, and vaccine design, and access to several high-performance computing clusters. Projects of interest include the development and optimization of deep learning-based approaches for modeling and design of antibody-antigen recognition, prediction and design of T cell receptor complex structures and T cell receptor specificity, and designing novel vaccine immunogens. The Pierce laboratory has assembled and curated large datasets of structures and affinity data, including an antibody-antigen docking and affinity benchmark (https://pubmed.ncbi.nlm.nih.gov/33539768/), the TCR3d database (https://pubmed.ncbi.nlm.nih.gov/31240309/), and the CoV3d database (https://pubmed.ncbi.nlm.nih.gov/32890396/), which can enable training of predictive algorithms. We recently utilized these datasets to assess antibody-antigen and T cell receptor-peptide-MHC modeling accuracy with AlphaFold2 (https://pubmed.ncbi.nlm.nih.gov/35900023/).
IBBR is a joint research enterprise of the University of Maryland, College Park, the University of Maryland, Baltimore, and the National Institute of Standards and Technology. IBBR leverages state-of-art integrative methods for bioanalytical, biophysical and structural characterization of biomolecules: cryo-electron microscopy, nuclear magnetic resonance, x-ray crystallography, small angle neutron and x-ray scattering and mass spectrometry. IBBR researchers seek to advance therapeutic development, biomanufacturing, and state-of-the-art measurement technologies, to support accelerated delivery of safe and effective medicines to the public. IBBR is a major initiative and supported in part by the University of Maryland Strategic Partnership: MPowering the State (MPower) , an initiative designed to achieve innovation and impact through collaboration.
POSTING DATE: 08/16/2022
CLOSING DATE: Open until filled. BEST CONSIDERATION DATE: 03/15/2023
HOW TO APPLY: Please visit https://ejobs.umd.edu/postings/104593
MINIMUM QUALIFICATIONS
Education:
- Ph.D. in bioinformatics, computational biology, computer science, biochemistry, or related field.
Experience:
- Experience developing and applying deep learning algorithms in structure prediction and/or design;
- Experience in computational protein modeling and design using software such as Rosetta and FoldX.
Knowledge, Skills, and Abilities:
- Excellent oral and written communication skills.
Preferred Knowledge, Skill, and Abilities:
- Familiarity with immune recognition, including modeling and design of antibodies or T cell receptors;
- Experience with Github and software development.
BACKGROUND CHECK
Offers of employment are contingent on completion of a background check. Information reported by the background check will not automatically disqualify you from employment.
DIVERSITY STATEMENT
The University of Maryland, College Park, an equal opportunity/affirmative action employer, complies with all applicable federal and state laws and regulations regarding nondiscrimination and affirmative action; all qualified applicants will receive consideration for employment. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, religion, sex, national origin, physical or mental disability, protected veteran status, age, gender identity or expression, sexual orientation, creed, marital status, political affiliation, personal appearance, or on the basis of rights secured by the First Amendment, in all aspects of employment, educational programs and activities, and admissions.
Women, minorities, LGBTQ+, veterans, and people with disabilities are encouraged to apply.