Up to 30% off — ends 2 Aug
ONLY00h00m00s
Up to 30% off — ends 2 Aug
ONLY00h00m00s
Xiaojing Gao
Top university
Just Landed
New Today
Computational Postdoc in Foundation Models for Intrinsically Disordered Proteins Stanford University in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Full funding availableCountry
United States
University
Stanford University

How do I apply for this?
Sign in for free to reveal details, requirements, and source links.
Apply for this position
Keywords
Suggested positions
About this position
Stanford University’s SynBioGaoLab is advertising a computational postdoc focused on foundation models and sequence-structure-function studies of intrinsically disordered proteins. The project is described as part of a consortium using unique large datasets and an interdisciplinary team that bridges several labs.
This opportunity is especially relevant for researchers with experience in machine learning, AI, protein design, and protein analysis. The post emphasizes computational work at the intersection of biology and computer science, with a strong biomolecular engineering/synthetic biology flavor.
The announcer is Xiaojing Gao, Assistant Professor at Stanford University. The post invites interested candidates to email the lab directly, but no formal application portal, deadline, or funding details are provided in the text.
If you are looking for a postdoctoral role in AI for biology, protein modeling, or data-driven biomolecular research, this appears to be a strong fit.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
How to apply
Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.
More information can be found here
Official Email
Ask ApplyKite AI
Professors

How do I apply for this?
Sign in for free to reveal details, requirements, and source links.