Berkman Klein Center for Internet & Society โ Fellowship Applications Now Open
The Berkman Klein Center for Internet & Society (BKC) at Harvard University is now accepting applications for its upcoming fellowship cohort. We invite scholars, technologists, activists, policymakers, and practitioners to propose and lead independent research initiatives aligned with BKCโs interdisciplinary AI research agenda.
Fellows selected through this call will thrive in cross-disciplinary environments, communicate effectively across technical and non-technical communities, contribute to collaborative research and student mentorship, and share BKCโs commitment to public interest technology and open, accessible AI research.
While we strongly encourage fellows to be in residence in Cambridge, Massachusetts, remote or hybrid arrangements may be considered case-by-case.
We welcome applications for two appointment periods:
- JanuaryโAugust 2026
- 2026โ2027 Academic Year (September 2026โAugust 2027)
Application Deadline: Friday, December 5, 2025 at 11:59 p.m. ET
About the BKC Fellowship Program
Since 1996, the Berkman Klein Center has advanced new models for interdisciplinary research, collaboration, and innovation. Unlike traditional academic fellowships, which focus narrowly on academic credentials, BKC values diverse forms of expertise, multisector experience, and a demonstrated record of public-interest impact. Our fellows include technologists, lawyers, researchers, designers, activists, artists, and policymakers whose work stretches across disciplines and sectors.
Research Priorities
For this cycle, BKC is prioritizing research that shapes the design, development, and governance of AI systems, with emphasis on:
1. Agentic AI
AI systems are rapidly shifting from passive tools to active agents capable of influencing social, economic, and political environments. Key gaps include:
- Lack of precise methods to measure and control agent behavior
- Unknown risks emerging from heterogeneous, multi-agent systems
- Governance, accountability, and institutional frameworks lagging well behind deployment
We seek work that helps build measurement tools, interpret multi-agent dynamics, and develop governance models for safe, accountable use of agentic AI.
2. Language Model Interpretability
Language models exhibit powerful behaviors while remaining opaque. This opacity limits safety, oversight, and trust. We welcome research that:
- Probes model internals and mechanisms
- Develops interpretability standards, institutional frameworks, and audit practices
- Bridges technical and socio-technical approaches to understanding model behavior
3. Benchmarking AI Systems Beyond โIntelligenceโ
Traditional benchmarks fail to capture dimensions of AI that increasingly matterโagency, identity, loyalty, metacognition, theory of mind, social cognition, situatedness, awareness, or subjective experience.
We aim to broaden evaluation to include these non-intelligence attributes, enabling policymakers, developers, and the public to better assess and govern advanced AI systems.
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Who Should Apply?
We welcome applications from:
- Established and emerging scholars
- Researchers and engineers
- Industry practitioners
- Policymakers and civil society leaders
- Artists and interdisciplinary thinkers
Applicants should demonstrate a track record of contributing to public or scholarly discourse and a commitment to public-interest technology.
Disciplinary Backgrounds
Our fellowship class spans computer science, engineering, law, policy, social sciences, arts, humanities, and beyond. Candidates should be prepared to carry out the work they propose and ideally bring interdisciplinary expertise.
Suggested Qualifications
For Applicants Proposing Technical Scientific Research
Strong candidates will typically have:
- A Ph.D. in Computer Science or a related field, or equivalent applied experience
- Expertise in Python and modern AI/ML frameworks (e.g., PyTorch, JAX)
- Primary-author publications at peer-reviewed CS venues or equivalent contributions
- Ability to communicate clearly with technical and non-technical audiences
For Applicants Proposing Research Engineering or Open-Source AI Infrastructure
Candidates should meet at least three of the following:
- Advanced degree or equivalent practical experience in CS or related field
- Expertise with Python and modern AI/ML frameworks
- Familiarity with agent frameworks (e.g., DSPy) and communication protocols (e.g., MCP, A2A)
- Experience with HPC systems (e.g., Slurm) and orchestration platforms (e.g., Kubernetes, Ray, Airflow)
- Hands-on work with open-weight models and related training/evaluation infrastructure
- A track record of building reproducible research systems (e.g., MLflow)
Application Requirements
Applicants will complete a brief questionnaire and upload the following PDF documents (max 5 MB each): Apply Here
- CV
- 1โ2 page cover letter
Describe your background, motivations, goals, and contributions to the BKC community. If relevant, note institutional deadlines that affect your ability to accept an appointment. - 2โ3 page project proposal
Outline the research you plan to pursue: the problem, methods, audience, and how it aligns with BKCโs research priorities. - 1โ3 work samples (combined into one PDF)
These should showcase the relevance or feasibility of your proposed work. - Contact information for two professional references
References may be contacted if you are named a finalist.
Application Questionnaire Overview
Applicants will be asked to provide information including:
- A 1โ2 sentence summary of the fellowship proposal
- Preferred fellowship period (JanuaryโAugust 2026, September 2026โAugust 2027, or both)
- Selected BKC research priority most aligned with the proposal
- Whether the research is technical, non-technical, or both
- Primary and secondary disciplines
- Work sector, home institution, and current title
- Funding pathway (BKC-funded up to $75K or externally funded)
- Amount of funding requested (if applicable) and whether acceptance depends on it
- Intended residency plan in Cambridge
- Previous BKC affiliations, if any
- Information on references
- Optional additional information