13 Aug Neural Interface ML/AI Engineer
THE TEAM
The Cognitive Neuroscience group at the Air Force Research Laboratory (AFRL) is seeking a neural interface machine learning/artificial intelligence (ML/AI) engineer to join a team of researchers located in Dayton, OH. Cognitive Neuroscience is one of the major core research areas (CRA) within the 711th Human Performance Wing (HPW) at the AFRL. The mission of this CRA is to enhance Airman performance through the understanding of cognitive, psychological, neurophysiological, and physical performance. The group consists of leading researchers in the fields of Cognitive Neuroscience, Cognitive Psychology, Neuromodulation, Neurobiology, Behavioral Neuroscience, and Biomedical Engineering. The team seeks to develop and validate assessments of cognitive state & readiness and performance predictions in both real-time and post- mission to inform and develop cognitive augmentation strategies, including closed-loop neuromodulation technologies. The group focuses on exploration, validation and development of neural (EEG, fNIRS, etc.), physiological (cardiorespiratory, etc.) and behavioral (eye tracking, posture, etc.) approaches for cognitive state assessment along with the development of neuromodulation and other augmentation paradigms for cognitive enhancement and assessing their safety and efficacy. Some of the ongoing research efforts within the group include studies focused on understanding the neural mechanisms associated with the degradation and enhancement of cognitive performance. In addition, the group is working to develop new, non-invasive brain machine interface (BMI) methods and technologies to read/write directly from/to the brain. The group has established many collaborations with industrial and academic partners and is funded by large research contracts from DARPA, ONR, NASA, LUCI, AFOSR, OUSD(R&E) as well as significant internal investments from AFRL.
THE ROLE
Your role as a neural interface ML/AI engineer is to develop, implement, and validate statistical and computational models that can interpret multimodal brain activity data and physiology measures to classify brain states that underlie learning/behavioral performance. In this role, you will work closely with a diverse team of neuroscientists, engineers, and psychologists at both the AFRL and leading institutions within academia and industry to develop, integrate and guide the development of cuttingedge signal processing techniques and decoding algorithms for use in neurotechnology-enabled performance optimization strategies. You will also design and help conduct human research studies to acquire the datasets necessary to develop computational models and convert research results into peer-reviewed publications and presentations in scientific forums and to senior leadership within AFRL.
ESSENTIAL FUNCTIONS
- Designs and executes human research studies that will generate datasets for cognitive and/or behavioral state prediction
- Interprets, analyzes, and develops classification algorithms using exploratory mathematical, machine learning, and statistical techniques based on scientific methods
- Presents results to internal and external stakeholders and in scientific conferences and other public forums
- Leads or assists in producing high quality technical papers and participates in IP generation
COMPETENCIES AND BASIC QUALIFICATIONS
- Master’s degree in computer science, statistics, neuroscience, cognitive neuroscience, human factors, biomedical/neural/electrical engineering or related fields
- Experience in manipulating, integrating, and analyzing large multimodal real-world physiologically-based datasets (e.g. EEG, fNIRS, MEG, autonomic, behavioral) using machine learning and statistical methods
- Strong background in using statistical (e.g. general linear modelling) and signal processing techniques (e.g. PCA, ICA) frequency domain (e.g. Fourier Transform methods, coherence) and time-frequency (e.g. wavelet) analysis
- Experience in utilizing classification algorithms (e.g. linear, neural networks, Bayesian, Riemannian geometry) as well as advanced techniques such as deep learning, transfer learning, ensemble learning.
- Ability to implement classification algorithms (e.g. adaptive classifiers) from datasets generated from human subjects in real-time
- Deep proficiency in MATLAB or equivalent programming language (C++, Python, R etc.)
- Knowledge in cognitive state and/or brain state classification is a plus
- Experience with human research is a plus
- Ability to develop software and implement hardware integration (e.g. digital and analog signaling, working with data acquisition systems) that enables closed-loop control of devices is a plus
- US citizenship is required
If interested, please contact:
Nathaniel Bridges, PhD
Neural Interfaces Team Lead
Research Biomedical Engineer
711th Human Performance Wing
Air Force Research Laboratory
nathaniel.bridges@us.af.mil
Sorry, the comment form is closed at this time.