Key Skills

Job Description

UCANACT is a pioneering brain-computer interface solution that converts brain signals from wearables and implants into computer commands and speech. Our platform enables continuous digital control and communication in end users’ homes, allowing individuals to regain independence and manage their smart home environment. By facilitating social participation and everyday interactions, UCANACT helps improve users’ wellbeing and supports their ability to live fuller lives. Role Description As a Machine Learning Intern at UCANACT, you will contribute to the development and improvement of models that translate neural signals into actionable commands and speech. On a day-to-day basis, you will assist in processing and analyzing data, implementing and testing machine learning and deep learning algorithms, and evaluating model performance using statistical methods. You will collaborate closely with the core team to prototype new approaches, document your findings and help integrate models into production-ready pipelines. This is a full-time, on-site role based in Utrecht, where you will work alongside a multidisciplinary team in a collaborative environment. Qualifications Currently enrolled in or recently completed a relevant bachelor’s or master’s program (e.g., Biomedical Engineering, AI, Data Science, Electrical Engineering or related field). Strong foundation in Biosignal processing, AI, Biomedical engineering, Data Science or similar. Understanding the nature and complexities of biological signals. Familiarity with ECoG, EEG, MUA or other electrophysiological data. Knowledge of Machine Learning and Deep Learning techniques, including model training, evaluation, and optimization. Understanding of Statistics for experimental design, data analysis, and performance metrics. Familiarity with programming languages commonly used in ML (e.g., Python) and frameworks such as PyTorch. Ability to work on-site in Utrecht, collaborate in a multidisciplinary team and communicate technical concepts clearly. Strong interest in brain-computer interfaces, neurotechnology and healthcare applications.

Core Responsibilities

Process and analyze neural signal data, implement and test machine learning and deep learning algorithms, and evaluate model performance using statistical methods. Collaborate with the team to prototype approaches, document findings, and help integrate models into production-ready pipelines.

Requirements

Applicants should be enrolled in or have recently completed a relevant bachelor's or master's program, with a strong foundation in biosignal processing, AI, biomedical engineering, data science, or a related field. They should understand biological and electrophysiological signals, know machine learning and statistical methods, and be familiar with Python and frameworks such as PyTorch.