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complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical approaches, the application of meta learning, and the integration of convex
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., Kubernetes), public cloud hybrid infrastructure, and administering AI/ML hardware (e.g., GPUs) is highly desirable. Demonstrated ability to program in administrative scripting languages (e.g., Python, shell
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, including Llama 2 from Meta and Gemma from Google for data quality. Job Duties and Responsibilities: Create a method for customizing LLMs specifically tailored to data phases such as data blocking, entity
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, aligning AI systems with complex human values, and building self-improving agents capable of autonomous learning. Our work combines cutting-edge experimentation – spanning RL, meta-learning, and robust
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signal processing AI/ML for communication systems Self-supervised ML, meta learning PHY/MAC optimization of wireless communication systems Robust, reliable, or explainable ML Experience with large-scale
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, including running large-scale machine learning models (e.g., PyTorch, JAX) on GPUs in an HPC environment and maintaining reproducible workflows. Extensive prior experience developing pipelines and analytic
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environment, and awareness of the FAIR principles and the role of meta-data for research data management. 2. Experience of some of the following technologies: tape storage, object storage, parallel file
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, and progression outcomes) and high-end compute (hundreds of NVIDIA H100 GPUs) via Mila and the Digital Research Alliance of Canada, and involves active collaborations with Stanford, Oxford, Google