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

Bakar Institute of Digital Materials for the Planet (BIDMaP); Berkeley (CA); Presencial; USD 65.000 - 90.000; Jornada completa

Descripción de la vacante

BIDMaP at UC Berkeley invites applications for Postdoctoral Fellows to join a cross-disciplinary program combining AI, ML, and data science with physical sciences to address climate challenges.

Eligible candidates hold a PhD awarded between January 2025 and August 2027 and may come from quantitative or domain science backgrounds, with a strong drive to integrate AI into discovery workflows.

Formación

• PhD or equivalent degree awarded between January 2025 and August 2027.
• Background in AI/ML, data science, statistics, or related fields.
• Interdisciplinary vision to apply computational methods to physical sciences.

Conocimientos

AI/ML

Data science

Statistics

Educación

PhD

Descripción del empleo

About the Opportunity

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

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.