Research Scientist: Hierarchical Sensorimotor Perception
Develops bi-directional/recurrent architectures for hierarchical sensorimotor perception inspired by brain principles to advance AGI. Requires PhD-level expertise in deep learning frameworks like PyTorch/JAX, theoretical rigor, and passion for biological intelligence.
About the job
Core Responsibilities
- Fundamental Research on Hierarchical Perception: Develop and implement new bi-directional/recurrent architectures for hierarchical perception. These models are expected to provide flexible querying, dynamically variable levels of abstraction, and representations that support planning/reasoning.
- Evaluation and Benchmarks: Design experiments to test and benchmark against existing architectures for out-of-distribution generalization.
- 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.
- 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: A demonstrated interest in the "big questions" of AI—robustness, generalization, and common-sense reasoning.
- Curious About the Brain: A 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.
Skills
PyTorch, JAX, Deep Learning, Graphical Models, Information Theory
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