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; collaborate with the research teams at EPFL and Imperial College London Design, implement, and maintain core components of the verified LLM inference engine, including the runtime and glue code connecting
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, protein purification, and/or machine learning is a clear plus. • Experience in data analysis and coding (preferably Python) • A systematic, independent working style and ability to work in a collaborative
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code Developing agentic AI workflows for specification autoformalization, proof generation, and proof repair Build a strong network in the fields of formal verification, systems, and ML infrastructure
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with the statistical grounding to reason about the models you use ● A GitHub account with code you have written ● Fluent English Desirable: ● Experience with single-cell or spatial genomics data ● Contributions to open
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(fluid dynamics) is highly beneficial Experience with statistical analysis, coding (R or Python), and digital modelling strongly preferred Willingness to travel (minimum to UK for experimental work
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) Ensuring medical grade software quality following industry best practices for patients' usage at home and for researchers in rehabilitation facilities Designing, coding, and driving the automation of test
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and working with agentic AI (e.g. Claude Code, Codex) Excellent communication skills in English Strong academic track-record and publication history The ability to collaborate in a multidisciplinary
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, OpenRouter) storing, querying, cleaning, and validating data using SQL/PostgreSQL applying NLP, statistical methods, and LLM-based approaches to textual data deploying and maintaining research code on Linux
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-on experience with biological data ● Strong Python skills and experience with PyTorch or JAX, together with the statistical grounding to reason about the models you use ● A GitHub account with code you have
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