Machine Learning Research Volunteer

Neura Via — Operations & Management Intern (Unpaid, Remote) Role

Machine Learning Research Volunteer

Unpaid (volunteer research Volunteership)

15 hours per week

Remote

Develop and refine model architectures for three modalities: cognitive test embeddings, voice test embeddings, and vascular biomarkers.

Implement modules including:

• Learned encoders and latent-space disentanglement
• R-GAT graph encoders for cognitive subdomains
• HuBERT-based audio embedding pipelines
• Custom latent fusion VAEs using product-of-experts rules
• Multimodal diffusion-transformer and LSTM hybrid networks for time-series trajectory prediction
• Bayesian heads for uncertainty estimation

Collaborate with the engineering and research teams to containerize experiments and prepare inference-ready MVP modules.

Contribute to documentation, reproducibility, and validation of models on synthetic and real datasets.

• Proficiency in Python and PyTorch.
• Must be incoming or currently enrolled in a Bachelors/Associates (or any equivalent) Program at a licensed post-secondary institution.
• Experience implementing sequence models (LSTM/GRU), Transformers, and VAEs.
• Familiarity with graph neural networks (GAT/R-GAT) and multimodal fusion methods.
• Comfortable reading and implementing from academic research papers.
• Strong debugging, version control (Git/Git Hub), and documentation skills.
• Availability of 15 hours per week with consistent progress updates.

• Prior experience working on multimodal or neuro-related ML projects.
• Understanding of probabilistic modeling, diffusion models, or Bayesian methods.
• Exposure to MLOps practices (Docker, experiment tracking, etc.).

• Collaborate on a real-world neuro-AI project with technical depth and publication potential.
• Gain experience in full-stack ML research from architecture design to deployment.
• Earn a detailed technical reference and recognition upon successful completion.

Interested candidates can apply via Linkedin Quick.

Applications are reviewed on a rolling basis. Please include your Git Hub, portfolio, and 1–2 examples of relevant ML work or projects.