Requirements

• Hands-on experience in biosignal processing, preferably EEG and ECG (experience with PPG/HRV is a plus).

• Strong understanding of signal processing techniques: filtering, ICA/PCA, wavelets, and feature extraction in time, frequency, and time-frequency domains.

• Good conceptual knowledge of linear algebra, probability, multivariate statistics, and optimization.

• Practical experience applying Machine Learning and Deep Learning for biosignal applications, including both classification and regression models.

• Proficiency in Python and ML/AI libraries such as scikit-learn, TensorFlow/PyTorch, Pandas, NumPy, MNE.

• Familiarity with cloud-based platforms (Google Colab, AWS, Azure) for training, testing, and deploying ML/AI algorithms.

• Experience in real-time biosignal processing pipelines for wearables or neurotech devices is a plus.

You should also ....

• Work collaboratively with the business team as they provide and refine the requirements.

• Be self-motivated, able to work independently with minimal direction, and be team-oriented with the ability to communicate to a wide variety of audiences.

• Strong analytical skills and strong decision-making capabilities.

• Hugely passionate and curious to work on deep technology.