Research Scientist: Energy Based Models
Conducts fundamental research on neuroscience-inspired energy-based models and hierarchical latent variable models for AGI. Designs experiments on cognition and collaborates cross-functionally to build scalable systems. Requires PhD-level expertise in deep learning and PyTorch/JAX.
About the job
Core Responsibilities
- Fundamental Research on Energy Based, Hierarchical Latent Variable Models: Develop and implement new architectures and learning/inference algorithms for neuroscience-inspired energy-based world models, with focus on representations that allow for parallel distributed planning.
- Scientific Discovery: 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.
Skills
PyTorch, JAX, Deep Learning, Graphical Models, Information Theory, Energy-Based Models, Latent Variable Models, Active Inference, Unsupervised Learning
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