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, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in
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institute is expanding this approach to detect a wider range of biological molecules, including volatiles in human breath. In parallel, the Institute is developing state-of-the-art methods that integrate
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properties (including structural, defect, electronic, ion transport, surfaces) and will link up with parallel experimental activities. Applicants should have a PhD in chemistry, physics or materials science
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 1 month ago
. The use case is a electrical substation implementing a process bus architecture conformal with IEC 61850 specifications. This includes redundant PRP (Parallel Reduncdancy Protocol) network architectures
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, mechanical and durability testing, and integration with advanced machine learning models. The postdoc will collaborate closely with CEBE’s parallel work packages. Experimental and analytical data generated in
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. Additional duties will include consulting some the tasks conducted by PhD students in our team. Enquiries regarding the role or the recruitment process can be addressed to prof. Agnieszka Janiuk (agnes
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, parallel storage systems and scientific data management. Recent research project details and outcomes can be found in computer systems conference proceedings, such as HPCA, FAST, SC, DSN, HPDC, IPDPS, and
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
is designed for candidates who have completed their PhD within the last two years and have experience as postdocs or industry researchers. This position offers a 12-month term with potential
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students. Qualifications: Required: PhD in Computer Science, AI, Data Science, Statistics, or related. Strong skills in machine learning and deep learning, with a fundamental understanding of LLMs
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models. Experience in large-scale deep learning systems and/or large foundation model, and the ability to train models using GPU/TPU parallelization. Experience in multi-modality data analysis (e.g., image