Researcher - DeepRAP Challenge

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📅 Posted 2026-09-09 · via Himalayas
🏷 AI-Research,Research-Scientist,Machine-Learning-Research,Neuro-Symbolic-AI,Deep-Learning-Researcher,Deep-Learning-Research,Deep-Learning-Research-Scientist
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What the Challenge is funding: Research that moves beyond today's deep learning/RL paradigms toward genuinely trustworthy cognitive AI; causal reasoning, abstraction, and planning under uncertainty. Funded projects don't just publish a paper, they build and demonstrate a working system (TRL3/4), help define new industry benchmarks for reasoning and trustworthiness, and join a wider EU-funded portfolio shaping how cognitive AI gets built and regulated across Europe.
The opportunity:

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Visibility at the frontier : this is exactly the kind of work that gets cited, gets you invited to speak, and gets you noticed by labs and industry doing serious reasoning/planning research

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A funded system, not a thought experiment : you're not writing a proposal that sits in a drawer; a successful award means building and demonstrating the actual architecture

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Named contributor on a flagship EU AI initiative: tied to benchmark development and portfolio activities the EIC is running across all funded DeepRAP projects

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A credential that compounds: EIC Pathfinder co-authorship is a strong signal on any postdoc, faculty, or industry research application going forward

What you'd do:

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Co-develop the scientific narrative and technical approach for a 30-page Pathfinder proposal

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Bring rigor on reasoning/abstraction/planning methodology, related work, and evaluation design

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Work directly with our founding team through submission

Who we're looking for:

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Research background in neuro-symbolic AI, causal inference, cognitive architectures, or deep RL/planning

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Track record: publications at NeurIPS/ICML/ICLR/AAAI or equivalent, PhD in progress or completed

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Based at or affiliated with a top research institution (ETH Zurich, Imperial College, EPFL, Oxford, TU Munich, etc.) EU/associated-country affiliation strongly preferred

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Comfortable working fast, iteratively, with a startup team, not academic-committee pace

Originally posted on Himalayas

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