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funded PhD position focused on the security, reliability, and efficient deployment of AI Transformer systems. The project examines how quantization, pruning, sparsity, KV-cache compression, and
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, memory safety, data race freedom, functional correctness) Implementing and verifying the inference coordination layer (batching, KV-cache management, scheduling) in Rocq/Lean, with extraction to executable
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Foundation Model inference across the cloud continuum, spanning two main tasks. Firstly, the post-holder will develop simulation-based and analytical methods to evaluate adaptive caching policies