Postdoctoral Fellowship in AI, Advanced Materials, and Planetary Health At BIDMaP, UC Berkeley

UC Berkeley, Bakar Institute of Digital Materials for the Planet; Berkeley (CA); On-site; USD 60,000 - 80,000; Full time

Job summary

BIDMaP (Bakar Institute of Digital Materials for the Planet) at UC Berkeley is inviting applications for Postdoctoral Fellows who will combine AI, machine learning, and data science with physical sciences to address climate challenges. The program targets recent PhDs in basic or data science with a passion for discovery-driven computation.

Start in spring/summer 2027, initial 2-year appointment with potential renewal, based at UC Berkeley’s Barbara and Gerson Gateway.

Qualifications

• PhD or equivalent degree awarded between January 2025 and August 2027.
• Advanced training in AI/ML, data science, statistics, or related computational methods.

Skills

AI/ML

Data science

Statistics

Computational methods

Education

PhD or equivalent

Job description

At BIDMaP (Bakar Institute of Digital Materials for the Planet) UC Berkeley, researchers are combining AI, machine learning, and data science with the physical sciences to confront climate change head-on.
BIDMaP’s Postdoctoral Fellows Program invites applications from recent PhDs in basic or data science fields who are passionate about using computation and discovery science to drive planetary solutions.
Applications Open: October 15, 2026
Application Deadline: December 1, 2026 for full consideration, review will continue until all available fellowships are filled
Target Start Date: Spring / Summer 2027
Duration: Initial 2-year appointment, renewable for a 3rd year upon mutual agreement.
Location: BIDMaP is housed in the beautiful new Barbara and Gerson Gateway at UC Berkeley

About You

Candidates should hold a PhD or equivalent degree awarded between January 2025 - August 2027 and have one of the following backgrounds

Quantitative focus:

Advanced training in AI/ML, computer science, statistics, or data science with a strong drive to apply computational methods to physical science problems.

Domain science focus:

Advanced training in chemistry, physics, biological sciences, or materials science with a strong vision for integrating AI/machine learning into discovery workflows.