PhD Studentship: British Open-ended Learning and Discovery (BOLD) Research Studentship

3.5-year D.Phil. studentship 

Project: Multiple projects available - information on specific project to be made available asap

Supervisors: Prof Jakob Foerster

BOLD has been created by folding 5 internationally leading labs into one effort—FLAIR and WhiRL (Oxford), DARK and LASP (UCL), and AIRL (Imperial). These labs have jointly published hundreds of papers in top venues and won best-paper awards at ICML, AAAI, and GECCO, and represent one of the highest concentrations of talent in Europe and globally. The founding faculty bring deep experience from the frontier labs shaping AI today.

BOLD consolidates existing world-class academic groups into a single co-located lab in Oxford, formally linked to all three universities. A lightweight three-phase research funnel—broad exploration, selective scale-up, then deep focused missions—lets students rapidly develop promising ideas into larger research projects while exploring new directions with minimal barriers, building on the team’s track record of turning early experiments into breakthroughs such as EGGROLL within months.

The labs have a proven pipeline from research to real-world impact: alumni have gone on to OpenAI, Anthropic, DeepMind, and the UK AI Security Institute, and have co-founded companies including Weco.ai (whose AIDE agent has been used by OpenAI and Meta), Helical, and the foundation-model unicorn Reflection AI.

Abstract:

There are three initial research pillars.

Beyond Backpropagation

Rethinking foundational neural network optimization. This pillar targets limitations of backpropagation (high communication overhead, poor handling of non-differentiable components, reduced plasticity) by developing zeroth-order, population-based, low-rank, and hybrid methods (e.g., the EGGROLL method) to unlock massive compute efficiency and decentralized, privacy-aware training.

Human-Centric Learning & Discovery

Pioneering multi-agent coordination as a core training component rather than an afterthought. This pillar initially builds a co-improving, human-in-the-loop "co-scientist" system based on zero-shot coordination frameworks, allowing AI agents to actively collaborate with human teams and leapfrog purely autonomous models.

Embodied Learning

Developing resource-agile paradigms for data- and compute-constrained physical systems (robotics/autonomous vehicles). Instead of monolithic cloud-dependent models, this focuses on autonomous skill discovery in simulation (e.g., OMNI-EPIC), resilient adaptation, modular hierarchies, and edge-optimized scalability for real-time onboard deployment.

Eligibility

These studentships are funded through a number of funders and are open to Home and overseas students (full award – home fees plus stipend).

Award Value

The studentship covers the full course fees plus a stipend (tax-free maintenance grant). The stipend is the UKRI minimum stipend for the first year plus £4,000, and at least this amount for a further two and a half years (pro-rata). 

Candidate Requirements

Prospective candidates will be judged according to how well they meet the following criteria:

• Applicants are normally expected to be predicted or have achieved a first-class or strong upper second-class undergraduate degree with honours (or equivalent international qualifications), as a minimum, in computer science, engineering, physics, mathematics, statistics or other related disciplines. A previous master's qualification is not required.
• Excellent English written and spoken communication skills

Application Procedure

If you have any technical questions about the DPhil Studentship, please email boldexec-team@eng.ox.ac.uk

Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria. Details are available on the course page of the University website.

Please quote 27ENGIN_JF in all correspondence and in your graduate application.

Application deadline: noon on 2 December 2026 (In line with the University admissions deadline set by the University)

Start date: October 2027

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