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of Chemical and Biological Engineering Posting Number R260133 Posting Link https://www.ubjobs.buffalo.edu/postings/64055 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment
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LLMs en infraestructuras GPU. - Análisis y evaluación de explicabilidad de las respuestas generadas por la IA generativa Area: Data Science and Natural Language Processing Tasks: - Development
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candidates hold a Master’s degree in Informatics, Mathematics, or a related field, and possess strong expertise in linear algebra, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13
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at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW for purposes of collective bargaining and matters affecting your
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. Participate actively in securing new funding. The contract will be fixed-term two-year full-time contract. The start date will be in January 2027. Where to apply Website https://selection.uoc.edu/web/profile
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heat transfer processes Use of advanced numerical methods (CFD, LBM, hybrid models) Utilization of high-performance computing (HPC, GPU) Analysis and validation of numerical results Optimization
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architectures and embedded platforms (ARM Cortex-M, NPU, FPGA, embedded GPU), e.g., via academic courses and/or project courses Research experience (e.g., through a Master thesis work or research internships) is
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in a university, research or comparable enterprise environment. Deep expertise in Linux and HPC architecture, including heterogeneous CPU/GPU compute, workload scheduling, high-speed interconnects
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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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goal is a fully verified opensource inference stack with competitive performance). Further information on the research of Dr. Marios Kogias can be found at https://marioskogias.github.io/ . Research