
29 May BCI Machine Learning and Signal Processing Engineers
AAVAA believes you should be in control of your devices. To do so, we are developing a solution by fuses advancements in multiple fields to enhance our capabilities as humans, opening the door to a new era of Human-Machine Interaction.
With AAVAA you get:
- The chance to solve one of the biggest challenges of human health
- Generous salary, benefits, and vacation program
- Work in the leading deep tech start-up incubator, TandemLaunch, surrounded by start-ups with specialists who are building the future with you
- A safe, healthy, and inclusive work environment
- Healthy snacks, frequent celebrations, endless tea and latte, social outings, and more!
BCI Machine Learning and Signal Processing Engineers
Engineers with experience in the following areas:
Basic skills:
- Machine learning (e.g., DNN, CNN, RNN, GAN, LSTM and SVM, KNN, Naive Bayes.)
- Deep learning frameworks (e.g., PyTorch, TensorFlow, Keras, Scikit)
- Advanced digital signal processing
- Experience with electrophysiological data analysis (EEG, MEG, etc.)
- High-level programming skills in Python, Matlab, C, C++, Java, etc.
- Fourier transforms, discrete signals, and filter design
- Time series data analysis
- Statistical methods such as Bayesian statistics and statistical inference
Preferred skills:
- Brain-computer interface and neuroscience
- Feature engineering and dimensionality reduction
- Knowledge of using cloud platforms
- Github
Above and beyond:
- 7+ years in artificial intelligence and related fields
- Advanced acoustics including techniques such as sound source separation, speech enhancement, noise reduction and cancellation, etc.
- Experience with firmware development of ARM based 32 bit microcontrollers
- Ability to lead a highly multidisciplinary team and execute goals
This position is also available through a Mitacs collaboration program with McGill University.
If this sounds like you, apply today at info@AAVAA.com!
Please click here to learn more.
Sorry, the comment form is closed at this time.