NeuraVia — Operations & Management Intern (Unpaid, Remote) Role Machine Learning Research Volunteer Type Unpaid (volunteer research Volunteership) Time Commitment 15 hours per week Location Remote What You’ll Work On 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. Requirements 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/GitHub), and documentation skills. Availability of 15 hours per week with consistent progress updates. Preferred 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.). Benefits 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. How to Apply Interested candidates can apply via Linkedin Quick. Applications are reviewed on a rolling basis. Please include your GitHub, portfolio, and 1–2 examples of relevant ML work or projects. #J-18808-Ljbffr
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