Job Summary
This position involves engaging in both fundamental and applied research in machine learning aimed at creating physiological world models pertinent to human biology. The role also includes the design of innovative architectures for multi-modal clinical telemetry systems and the establishment of robust evaluation benchmarks focused on predicting disease trajectories.
Responsibilities
- Engage in research that bridges fundamental and applied machine learning.
- Develop physiological models that accurately represent human biological systems.
- Design and implement novel architectures for handling multi-modal clinical telemetry.
- Create and maintain reliable evaluation benchmarks for forecasting disease trajectories.
Qualifications
- A PhD in Computer Science, Machine Learning, or a closely related discipline.
- At least 2 years of relevant experience in building adaptive systems and foundation models.
- A strong publication record with first-author contributions in leading machine learning conferences and journals.
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