Under the guidance of a mentor and in a highly collaborative environment, the participant will have the opportunity to gain experience about hybrid machine learning approaches, including large language models, and cutting-edge protein tertiary structure predictions to develop and apply AI methods to enhance the functional annotation space of multiple species by predicting protein dynamics and protein interactions with the aim of supporting the improvement of agronomically-important traits. The participant will be part of the GrainGenes (wheat.pw.usda.gov) biological database project located in the beautiful San Francisco Bay Area.
Participant Stipend: The participant will receive a monthly stipend commensurate with educational level and experience. The participant will receive an estimated annual stipend of $80,000, health insurance stipend supplement of $8,000 per year, a science and research travel allowance up to $3,000 per year.
Preferred skills:
Experience in computer science, bioinformatics, or computational biology
Experience in machine learning and artificial intelligence
Experience with working with genetics and genomic data
Experience working with large, diverse datasets and data mining approaches
Proficiency in Linux and computational programming
Strong computational and analytical skills
Strong oral and written communication skills
A complete application consists of:
- An application
- Transcript(s) – For this opportunity, an unofficial transcript or copy of the student academic records printed by the applicant or by academic advisors from internal institution systems may be submitted.
- A current resume/CV, including academic history, employment history, relevant experiences, and publication list
- Two educational or professional recommendations
- A copy of an abstract or reprint of an article
Apply at www.zintellect.com/Opportunity/Details/USDA-ARS-PW-2024-0008