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of advanced language models and derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development for scalable
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platforms, GPU environments or scientific computing. Experience in EU-funded projects or international collaborations. Experience working with learning management systems or digital learning tools. Knowledge
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Selectris energy filter and Falcon 4i. The Crick Institute has excellent High Performance Computing resources, dedicated high-speed data storage and CPU and GPU clusters. In collaboration with the Cryo-EM
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team, potentially across different time-zones Discipline and regard for confidentiality and security The job descriptions for each role contain detailed person specifications. Interdisciplinary research
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Ability to work collaboratively, as part of a large, distributed team, potentially across different time-zones Discipline and regard for confidentiality and security The job descriptions for each role
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 6 hours ago
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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algorithms Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
accurately distinguish specific aphid-morphological traits (wings/no wings, estimated weight, different developmental stages and color differences, etc). Screen a pea collection for aphid resistance in
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accurately distinguish specific aphid-morphological traits (wings/no wings, estimated weight, different developmental stages and color differences, etc). Screen a pea collection for aphid resistance in
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for mechanical, electrical, cooling, and infrastructure systems that underpin Cornell’s computing environment, including High-Performance Computing (HPC) and Graphics Processing Unit (GPU)‑intensive workloads