Research Scientist, Neural Interfaces
il y a 6 jours
We're looking for people who want to shape the future of this technology and leverage state-of-the-art ML to push the limits of non-invasive neuromotor interfaces.
Responsibilities
- Research and develop machine learning models that leverage biosignals to advance neural interface capabilities
- Build cutting-edge machine learning and signal processing models (event detection, sequence-to-sequence, signal separation, time series regression, data compression, data augmentation etc.)
- Use quantitative research methods to define, iterate upon and advance key areas of our research agenda
- Obtained a PhD in the field of computational neuroscience, machine learning, computer science, electrical engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
- Research-oriented software engineering skills, including fluency with libraries for scientific computing (e.g. SciPy ecosystem) and machine learning (e.g. Scikit-learn, PyTorch, JAX, TensorFlow)
- Proficiency with quantitative methods (mathematics, statistics) and experience learning new technical knowledge and skills rapidly
- 3+ years of experience working autonomously to design, execute, interpret, and present research studies
- Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Experience working and communicating cross-functionally in a team environment
- Experience in signal processing and familiarity with real-time signals
- Experience with large scale cluster computing for machine learning modelling
- Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, COSYNE or similar
- Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
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