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MATS Program Autumn 2026: Your Path to AI Safety Research

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MATS Program Autumn 2026: Your Path to AI Safety Research

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MATS Program Autumn 2026: A Deep Dive for Aspiring AI Safety Researchers

The field of Artificial Intelligence (AI) is rapidly advancing, and with it comes a growing need for experts dedicated to ensuring its safe and responsible development. For those looking to contribute to this critical area, the MATS Program offers a unique opportunity. This 10 to 12-week research fellowship is designed to train and support emerging researchers and field-builders focused on AI alignment, interpretability, governance, and security. The Autumn 2026 cohort is now accepting applications, presenting a chance to work alongside leading minds and gain invaluable experience in AI safety.

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What is the MATS Program?

The MATS Program is a structured research fellowship aimed at fostering the next generation of AI safety professionals. It provides a supportive environment where fellows can collaborate with world-class mentors and engage in research and field-building efforts. The program’s core mission is to reduce risks associated with advanced AI systems. Fellows are based in either Berkeley or London, immersing themselves in a community dedicated to making AI safer and more reliable.

Program Structure and Support

During the 10 to 12-week duration, fellows receive comprehensive support to maximize their research output and career development. This includes dedicated research management assistance, access to cutting-edge resources, and guidance from experienced mentors. The program is structured to allow fellows to concentrate fully on their research and contribute meaningfully to the AI safety field. The Autumn cohort specifically connects fellows with mentors from prominent organizations such as Anthropic, OpenAI, Google DeepMind, Redwood Research, and ARC.

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Benefits for Fellows

Participants in the MATS Program can expect substantial benefits designed to facilitate their research and living needs. The program offers a stipend of $12.5k, along with $20k for compute resources, enabling fellows to undertake demanding computational tasks. Housing and meals are provided free of charge, removing significant financial and logistical burdens. Furthermore, travel expenses are covered, and if necessary, assistance with obtaining a J1 visa is provided, making the program accessible to international applicants.

Eligibility Requirements

The MATS Program welcomes applicants from a wide array of academic and professional backgrounds. While a background in machine learning, mathematics, or computer science is common, the program also values individuals from fields such as policy, economics, physics, and cognitive science. The most important qualifications are a strong commitment to advancing AI safety and demonstrated technical aptitude or research potential. Prior experience in AI safety is beneficial but not a mandatory requirement for application.

Application Process

The application for the MATS Program is designed to be straightforward yet thorough. It begins with a basic profile submission. Applicants will also have the opportunity to include their publication record, if applicable. The core of the application involves responding to two short essay questions, which allow candidates to showcase their thinking and motivation. Finally, applicants select their preferred research tracks, and some tracks may require additional short responses. There are no separate stream selections within the main application form.

Frequently Asked Questions

What is the main goal of the MATS Program?
Where are the MATS Program fellows based?

Fellows are based in either Berkeley or London during the program.

What kind of support does the MATS Program offer fellows?

The program provides a stipend, compute resources, free housing and meals, and covers travel expenses.

Do I need prior experience in AI safety to apply?

Prior experience in AI safety is beneficial but not required; a strong commitment to AI safety and technical aptitude are key.

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Posted in: Conferences Fellowships Grants

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