Gerbrand Ceder
Berkeley EECS (CS)
Research focus: machine learning in materials science
手动审核申请准备：以材料科学问题为切入点，引入 ML 或自主实验，以揭示或优化可发现的内容。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: machine learning in materials science · Advancements in Battery Materials).
- Industry research or startup/translation paths appeal to you (meaningful share in public outcomes).
- Treat this as a strong ecosystem signal for computational materials, batteries, autonomous labs, and Materials Project-style infrastructure, with alumni details needing a dedicated CV/publication-tree
- 你想把 ML 接到真实数据与落地问题（当前信号：machine learning in materials science · Advancements in Battery Materials）。
- 工业研究或创业/转化路径对你有吸引力（公开去向里占比不低）。
- 请将其视为计算材料、电池、自主实验室及材料项目基础设施的强生态系统信号，并需为脱ai的校友提供专门的CV/出版物树。
What to watch for / 什么情况下要慎重
- You want pure theory ML with little domain data or cross-disciplinary collaboration.
- Faculty-only is your sole goal and you want zero industry/translation exposure.
- 你要的是纯理论 ML、几乎不碰领域数据或跨学科合作。
- 你把「教职为唯一目标」且完全不想碰工业/转化网络。
Strong public placement signal · 公开去向信号：强
Public evidence as of 2026-06-10
These are decision-support signals compiled from public evidence (faculty pages, publications, lab sites) to help you ask better questions — not a ranking, rating, or allegation about this advisor. / 以上为基于公开信息整理的择校参考，帮助你提出更好的问题，并非排名、评分或对该导师的指控。
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