AAVAA is a Montréal-based start-up that is building a listening device that selectively hears what you desire to hear without any cumbersome assumptions. Our solution capitalizes on the novelty of Brain and Biosignal Synthesis, the robustness of Acoustics, and the strength of Artificial Intelligence. Come work alongside passionate entrepreneurs!
Positions are available at different levels, from interns to visiting graduate and postdoc trainees enrolled in a university, to full-time employees.
To apply, please send your cover letter, CV or resume, and contact information for 2 references to info@AAVAA.com.
Mitacs Interns
PostDoc/PhD · Full time
Three positions are available at the Postdoc level or for graduate students currently enrolled in a Canadian university through a Mitacs collaboration between AAVAA and McGill University (Prof. Sylvain Baillet). We are looking for a Computational Neuroscientist, Machine Learning Engineer, and Audio Enhancement and Signal Processing Engineer.
The positions are for the duration of 12 months, with the ability to renew the contract at the end of each term. In line with social distancing, the positions may initially be remote, which may evolve to be in-office positions located at AAVAA as the situation changes.
Please see below to ensure you have experience in several of the related areas.
Computational Neuroscientist
- Electrophysiological data (M/EEG)
- Advanced digital signal processing
- Machine learning, including deep learning algorithms, such as DNN, CNN, RNN, GAN, LSTM
- Time-frequency and functional connectivity analysis
- Real-time brain-computer interface
- Auditory neuroscience and psychology of hearing
- High-level programming in Python and Matlab
Machine Learning Engineer
- Machine learning, including deep learning algorithms, such as DNN, CNN, RNN, GAN, LSTM
- Classical machine learning approaches (e.g., SVM, KNN, Naive Bayes)
- High-level programming in Python, Matlab, C, C++, Java, etc.
- Deep learning frameworks (e.g., PyTorch, TensorFlow, Keras, Scikit)
- Time series data analysis
- Feature engineering and dimensionality reduction
- Statistical methods such as Bayesian Statistics and statistical tests
- Knowledge of using cloud platforms
- Github