Research Scientist: Hierarchical RL Agents
Conducts fundamental research on hierarchical RL agents, developing architectures for world modeling, planning, and ethologically constrained learning. Collaborates cross-functionally to translate brain-inspired principles into scalable AGI systems; requires PhD-level expertise in AI/ML and strong coding skills.
Core Responsibilities
- Fundamental Research on Hierarchical World Model Learning from Experience: Develop and implement new architectures and learning/inference algorithms for hierarchical RL that supports hierarchical world modeling and hierarchical planning.
- Ethologically constrained learning: Design tasks to evaluate the agent in ethologically constrained environments, and benchmark different agent designs. Design experiments to test hypotheses regarding high-level cognition, causal representations, sensory-motor integration, active inference, and unsupervised learning.
- Cross-Disciplinary Collaboration: Work alongside neuroscientists and software engineers to translate abstract mathematical frameworks into scalable systems.
- Contribute to Open Science: Help innovate new publication models that incentivize speedy dissemination, open source code releases, free open access, and impact measurements based on uptake.
Desired Qualifications
- Technical Depth: Preferred: PhD in Computer Science, Electrical Engineering, Neuroscience, Physics, or a related quantitative field. Open to exceptional candidates with non-traditional educational histories.
- Theoretical Rigor: Strong foundations in deep learning, graphical models, and information theory.
- Coding Proficiency: Expert-level skills in deep learning frameworks (PyTorch/JAX), with a focus on clean, reproducible research code.
- AGI Mindset: Demonstrated interest in the "big questions" of AI—robustness, generalization, and common-sense reasoning.
- Curious About the Brain: Genuine passion for exploring the computational and architectural principles of the mammalian brain.
- Startup DNA: Ability to thrive in a lean, fast-paced environment where you have high autonomy and a direct influence on research direction.
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