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to closely collaborate with CS faculty members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience
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MOHAMED BIN ZAYED UNIVERSITY OF ARTIFICIAL INTELLIGENCE | Dhabi Kalan, Haryana | India | about 1 month ago
, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM datasets in the
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the Parallel Systems Lab ( https://psl-ntu.github.io ) at NTU’s CCDS. The role focuses on designing, developing, and evaluating efficient, scalable systems for modern multi-agent HPC-AI workflows
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View more categories View less categories Discipline Computing Job Type Faculty Member Employment - Hours Full time Duration Permanent Qualification PhD Sector Academia Apply on website (This will open
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Center (HT- RDS Center). The applicant would be responsible for designing and developing workflows for automated, micro-scale parallel experimentation. These workflows will be used for the
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<span lang="en">TUD Dresden University of Technology</span> | Dresden, Sachsen | Germany | about 13 hours ago
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At the Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, the Chair of Highly-Parallel VLSI Systems and Neuro
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publications, conference papers and technical manuals Basic Qualifications: A PhD in Computational Chemistry, Physics, Chemical Engineering, Materials Science and Engineering, or a related field completed
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assistant professor or associate professor whose research advances parallel and high-performance computing. Relevant areas include computer architecture, operating systems, compilers and the
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Lab researches on a variety of computer systems topics including HPC resilience, data center power management, large-scale job scheduling and performance tuning, parallel storage systems and
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
modelling from Project 1, we will conduct focus groups and interviews with communities in high- and low-risk zones to capture local knowledge, behaviours, and risk perceptions related to AMR. In parallel