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About the Role
Synaptix is developing non-invasive brain-computer interfaces by treating neural decoding as a fundamental machine learning problem. We are seeking exceptional researchers to develop new models, datasets, and hardware to learn neural dynamics and translate them into real-time control of machines. Prior experience in neuroscience or BCIs is not required; we prioritize exceptional research ability, mathematical depth, and the capacity to develop novel solutions for complex modeling challenges.
Responsibilities
- Develop new machine learning methods for modeling high-dimensional neural and behavioral data.
- Learn latent structure and dynamics from noisy, non-stationary, partially observed time-series data.
- Develop approaches to neural decoding that generalize across people, sessions, tasks, and recording conditions.
- Explore problems at the intersection of deep learning, dynamical systems, system identification, control, information theory, optimization, and statistical learning.
- Investigate self-supervised and unsupervised learning methods for large neural datasets without dense behavioral labels.
- Design rigorous experiments to understand model scaling, generalization, representation quality, and the limits of non-invasive neural decoding.
- Build simulations and generative models for studying neural signals and testing hypotheses.
- Work closely with researchers collecting neural datasets and engineers building sensing hardware.
- Translate promising research into real-time systems controlling computers, communication interfaces, wheelchairs, prosthetics, and other machines.
- Build rigorous, reproducible implementations of research ideas and scale successful approaches.
- Contribute original research that advances Synaptix's systems and the broader scientific understanding of neural decoding.
Requirements
- PhD or equivalent demonstrated research ability in machine learning, computer science, applied mathematics, physics, statistics, computational neuroscience, electrical engineering, or a related technical field.
- Evidence of exceptional ability to conduct original research.
- Strong mathematical foundations in areas such as linear algebra, probability, optimization, statistics, information theory, or dynamical systems.
- Strong programming ability and experience implementing and evaluating machine learning models in PyTorch, JAX, or equivalent frameworks.
- Experience working with high-dimensional, sequential, scientific, sensory, or otherwise complex datasets.
- Ability to take an ambiguous research problem from first principles through formulation, experimentation, analysis, and implementation.
- Ability to operate independently, question existing assumptions, and pursue technically ambitious ideas.
Skills
- Representation learning
- Self-supervised learning
- Foundation models
- Generative modeling
- Time-series modeling
- Sequence modeling
- Latent-variable models
- State-space models
- Dynamical systems
- System identification
- Scientific machine learning
- Inverse problems
- Reinforcement learning
- Optimal control
- Information theory
- Statistical physics
- Computational neuroscience
- Neural signal processing
- Multimodal learning
- Large-scale distributed model training
- PyTorch
- JAX
Education Level
- PhD or equivalent demonstrated research ability
Benefits
- Comprehensive health benefits
- Paid holidays
- Unlimited PTO
About the Company
- Synaptix is building non-invasive brain-computer interfaces by treating neural decoding as a fundamental machine learning problem.
- We are developing new models, datasets, and hardware to learn these dynamics and translate them into real-time control of computers, communication systems, mobility devices, and eventually a much broader class of machines.
- We are a small research-driven team working on problems where there is no established playbook.
- We value first-principles thinking, mathematical and experimental rigor, intellectual honesty, speed, and researchers who are willing to question assumptions about what should be possible with non-invasive neural signals.
- We care more about important results than credentials, titles, or adherence to a particular modeling paradigm.
- Our goal is to make non-invasive brain-computer interfaces capable enough to restore communication and mobility to people with severe disabilities, and ultimately to create a general interface between the human brain and machines.