Signal Processing Scientist
Culver City, CA /
Machine Learning | Data Science /
Permanent | Full-time
Kernel is building world leading, non-invasive brain interface technology.
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
- PhD in electrical engineering, bioengineering, physics, or related quantitative field
- Experience working with time dependent neuroimaging data in particular including electromagnetic (EEG, ECoG, MEG) and hemodynamic modalities (fNIRS, fMRI)
- Experience with linear or nonlinear source reconstruction for neuroimaging data (EEG, MEG, fNIRS)
This position will require access to information protected under U.S. export control laws and regulations, including the International Traffic in Arms Regulations (ITAR) and/or the Export Administration Regulations (EAR). Please note that any offer for employment will be conditioned on authorization to receive software or technology controlled under these U.S. export control laws and regulations without sponsorship for an export license.