We are looking for an experienced signal processing scientist to support algorithmic development, processing, and analysis for the neural data from our new technologies. The ideal candidate has an intuitive grasp of a rich portfolio of filtering, de-noising, artifact removal, dimensionality reduction, and analysis techniques for various modalities of neurophysiological time series data. A core aspect of the role will be to develop, adapt, and optimize algorithms for our MEG and TD-fNIRS data in particular.
Neuroscience is the new rocket science.
- Rapidly research, implement, validate, and extend (novel) processing and analysis algorithms for noninvasive neural time series signals
- Understand the specific noise, artifact, and contamination properties of our physical sensor hardware in various operational contexts and optimize algorithms to extract maximal information content
- Communicate progress, challenges, and results effectively to team members
- Master’s degree in electrical engineering, bioengineering, physics, or related quantitative field with strong mathematics and statistics background
- Practical and theoretical mastery of linear algebra and signal processing
- Deep understanding of linear systems, Fourier analysis, wavelet analysis, time-frequency analysis, artifact detection and removal, and convex optimization
- Experience applying signal processing to (neuro-)physiological time series data
- 3+ years of industry experience building and deploying advanced signal processing algorithms
- Industry-grade coding and software engineering skills. The candidate should be able to write clean, efficient, and well-documented code in python; deeply understand decomposition, modularity, abstraction, and object-oriented programming; be comfortable with numerical python (numpy, scipy, matplotlib); and be familiar with collaborative software development including version control and code reviews