Job Description

NeuraVia — Operations & Management Intern (Unpaid, Remote)RoleMachine Learning Research VolunteerTypeUnpaid (volunteer research Volunteership)Time Commitment15 hours per weekLocationRemoteWhat You'll Work OnDevelop and refine model architectures for three modalities: cognitive test embeddings, voice test embeddings, and vascular biomarkers.Implement modules including:Learned encoders and latent-space disentanglementR-GAT graph encoders for cognitive subdomainsHuBERT-based audio embedding pipelinesCustom latent fusion VAEs using product-of-experts rulesMultimodal diffusion-transformer and LSTM hybrid networks for time-series trajectory predictionBayesian heads for uncertainty estimationCollaborate 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.RequirementsProficiency 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.PreferredPrior 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.).BenefitsCollaborate 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 ApplyInterested 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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