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: 2023983 Carnegie Mellon University is a private, global research university that stands among the world's most renowned education institutions. With ground-breaking brain science, path-breaking performances
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inconsistent accuracy, creating a growing need for systematic validation and performance assessment. At ING, large document flows and multiple RAG-based initiatives call for robust mechanisms to evaluate model
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manufacturing and materials for extreme environments. Generate original research ideas and lead innovative projects that result in multiple high-impact publications annually. Mentor doctoral students and engage
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anthropology Experience in R, Python, and/or a comparable programming language, preferably in a High-Performance Cluster Unix computing environment How to Apply: For full consideration, please submit
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programming skills, excellent communication and organisational abilities, and experience working both independently and collaboratively, with additional merits including high-performance computing experience
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Application Deadline 10 Jan 2026 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances
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to analyzing data Knowledge of high-performance computing, such as parallelization, the use of C++, or interfacing with specialized linear algebra packages Other Information: Work arrangement: On-site Candidates
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generative AI models through Cornell's AI gateway and high-performance computing resources via Empire AI. This position provides opportunities to collaborate with leading AI companies, such as Anthropic and
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define requirements and performance specifications for future HEP/NP detector systems Perform detector concept development, system-level design, and optimization leveraging emerging computing architectures