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About the role
As a Research Engineer - Brain Computer Interface Models , you will be a core contributor to Zyphra’s BCI work, building the next generation of open-source EEG and brain–computer interface models. You will be involved across the full model lifecycle, from data collection and preprocessing to designing novel architectures and training methodologies.
What they're looking for
- Organized, methodical researcher who can take ownership of a project component, communicate their process clearly, and deliver a working module
- Excellent communication and collaboration skills, with clean and concise coding practices
- Strong implementation and prototyping skills, with comfort working across both research and engineering at scale
- Ability to learn new domains quickly, orient oneself in the academic literature, and implement new ideas, creatively borrowing concepts from other modalities and applying them to EEG domain
- Interest in neural data such as EEG, MEG, fMRI as well as building foundation models in a much less explored space
- Proficiency with PyTorch and Python
More about this role
As a Research Engineer - Brain Computer Interface Models , you will be a core contributor to Zyphra’s BCI work, building the next generation of open-source EEG and brain–computer interface models. You will be involved across the full model lifecycle, from data collection and preprocessing to designing novel architectures and training methodologies.
Large-scale EEG model training runs and evaluation
Architecture and training methodology ablations and innovations
Dataset collection and preprocessing for EEG and other BCI modalities
Performance optimization of the training stack
Integration of models into real-world BCI applications
Organized, methodical researcher who can take ownership of a project component, communicate their process clearly, and deliver a working module
Excellent communication and collaboration skills, with clean and concise coding practices
Strong implementation and prototyping skills, with comfort working across both research and engineering at scale
Ability to learn new domains quickly, orient oneself in the academic literature, and implement new ideas, creatively borrowing concepts from other modalities and applying them to EEG domain
Interest in neural data...
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