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positions are closely linked, and candidates will have the opportunity to collaborate together and learn methods from the parallel project, building a broad skillset. Both positions are part of the larger
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opportunity to collaborate together and learn methods from the parallel project, building a broad skillset. Both positions are part of the larger project “Bringing the wild into the lab with Virtual Reality
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metabolites in embryo culture media, with a particular focus on nutrient and metabolite uptake by developing embryos. In parallel, the project will apply our previously developed low-input detection methods
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requiring initiative and judgment by applying knowledge and understanding of federal and state-level civil procedure to make informed decisions about data categorization. Aggregate and review court filings
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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contribute to the CELECT work package on AI-Assisted Engineering. The research will focus on developing AI-assisted methods for parallel hardware-software co-design of mechatronic systems, enabling virtual
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will play a key role in building a parallelized, agent-driven exploration system and integrating a multimodal detection pipeline, ensuring real-time performance, scalability, and deployment readiness in
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learning frameworks (e.g. PyTorch, TensorFlow) and relevant libraries. Practical experience inscalable data processing, including the use of parallel computing, cloud platforms,and distributed systems