MARS Programme

Time Commitment

Participants should expect to each spend 8-15+ hours/week on their project, depending on the project chosen.

Weekly Check-Ins

Throughout the duration of the program, you are expected to check-in:

With your teammates once a week. It'll be up to your team to coordinate meeting times or create co-working systems.

With your supervisor once a week.

The format of check-ins with your supervisor will be at their discretion.

You will also be assigned a research manager who will support your work throughout the programme. While you'll coordinate with your team, the research manager provides external accountability and guidance to help keep your project on track.

We will also have program-wide events and check-ins, including socials, co-working sessions, and guest speakers. More details on these events will be announced to accepted participants.

MARS Research Symposium

This event will take place at the end of the program, where each research group presents their research.

Who should apply to mentor?

We're looking for mentors with:

Research experience in AI safety (technical) or AI policy/governance

Well-scoped project proposals achievable in 2-3 months part-time

Research management skills for weekly team guidance

Time commitment

Mentors are free to choose when and how they meet with mentees. We expect mentors to commit a minimum of ~8 hours in total to the project in the kick-off week, and weekly or biweekly check-ins with teams during the remote phase

Virtual mentorship is possible, but we may give preference to those who can come to the in-person kick-off week in Cambridge

Compensation & benefits

~$30/hour for mentorship time (we will compensate mentors up to 20 hours in total during the kickoff week and 8 hours/week during the remote phase)

Accommodation during Cambridge week

Travel support: ~$200 (UK/EU), ~$650 (international)

Office access and research assistance from participants

Co-authorship opportunities and networking

Who will I mentor?

PhD researchers from highly technical fields transitioning into AI safety

High-performing Master's students with strong AI safety context and understanding

Technical professionals pivoting to safety work, bringing robust software development and research experience

Nearly all candidates have excellent technical skills, and most bring existing AI safety and governance knowledge to the role.