At Rippletide, we’ve built the first Hypergraph Decision Database, a foundation that makes agents deterministic, explainable, and compliant by design. By moving decisions out of LLMs and into a reasoning database, we eliminate hallucinations and bring full transparency to every action. Born during a founder’s time at Imperial College London (ICL), we are an AI company developing advanced reasoning systems for autonomous agents.
We are opening three PhD collaboration opportunities for researchers at Imperial College who want to apply their expertise to impactful, real-world problems. You will work at the intersection of language models, reasoning, and decision-making to build the next generation of reasoning architectures.
Collaborative R&D: Work closely with Rippletide’s R&D team on cutting-edge reasoning architectures.
Real-World Application: Apply research to complex problems and data sets involving autonomous agent decision-making.
Academic Contribution: Publish and co-author papers or demonstrations based on the joint research work.
Multi-Agent Reasoning Systems: Focus on designing alignment mechanisms, coordination strategies, and communication protocols for collective agent reasoning in dynamic environments.
Model Predictive Control (MPC) for Decision Optimization: Focus on how predictive models reason over uncertainty and maintain optimality when faced with evolving real-world business constraints.
Neuro-Symbolic Reasoning: Focus on integrating neural network adaptability with structured symbolic systems to build explainable and generalizable hybrid models.
The Academic Specialist: You are a current PhD student with a strong background in one of the following: multi-agent systems, reinforcement learning, control theory, optimization, applied mathematics, or neuro-symbolic AI.
Research Focus: You have experience in fields like decentralized decision-making, adaptive MPC, hybrid control architectures, or reasoning under uncertainty.
Technical Curiosity: You have a strong interest in the intersection of learning, logic, and simulation.
High Impact: Apply your research to real-world problems that solve the #1 barrier to AI adoption: reliability.
Cutting-Edge Tech: Work with a product that moves decisions into a reasoning database to eliminate hallucinations.
Compensation: Competitive compensation to be discussed during the interview process.
