Postdoctoral Appointee - Federated Learning and Foundation Model
Core
Conduct research on optimization and training dynamics for large-scale machine learning, designing algorithms for distributed and federated settings.
Role type
Postdoctoral Researcher (Machine Learning Theory & Optimization)
Builds
Open-source research software and reproducible experiments
Domain
Scientific computing, Federated Learning, Foundation Models
Deliverable
production ML models
Required skills
Python, PyTorch, Deep Learning, Stochastic Gradient Descent, Distributed Systems, Mathematical Analysis, Research Publication
Preferred skills
Stochastic Optimization, Differential Privacy, LoRA, Multi-node Distributed Training, Open-source Contributions
Responsibilities
Conduct research on optimization and training dynamics, Design and benchmark new training algorithms, Contribute research software to open-source projects, Publish and present results at leading conferences, Mentor students
Seniority
Postdoctoral, Independent Researcher
