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computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI environments and tools. Prior experience
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electricity prices with focus on Nordic electricity market including implementation, test and validation at the DTU Risø HPP facility (possibly in a GPU computing infrastructure) Aid the implementation of IEA
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allowance. Generous travel, equipment, and publication funds. Access to NYUAD’s world-class research facilities, including a high-performance computing (HPC) cluster with ~30,000 cores and 34 GPU nodes. Start
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be direct access to advanced biophysical infrastructure in the biophysics core facility headed by the PI, a GPU cluster with working pipelines for computational design and the department’s bioimaging
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at the workplace, from compute and GPU servers to supercomputers Opportunity for a PhD (Dr. rer. nat.) in one of the group’s diverse research areas Salary according to the public service pay scale (TV-L E13
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: Ability to work with large structured and unstructured datasets, and GPU-accelerated computing. Proven experience with Large Language Models. Required Skill/Ability 3: Sound background in theoretical and
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. Experience in parallel programming (MPI, GPU, etc.). Proficiency in biostatistical methods. Ability to work independently and in group settings. Ability to learn quickly and apply new analytic techniques. Job
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analysis, and GPU/FPGA-based acceleration. Ability to work in a multidisciplinary team, collaborate with industry and international partners, and contribute to the design of next-generation electron imaging
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the models and algorithms on GPUs and mainframe computing platforms. Essential Function Yes Percentage of Time 40 Job Duty Mentoring graduate and undergraduate students. Assist the PI (Qi Wang) to mentor
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allowance. Generous travel, equipment, and publication funds. Access to NYUAD’s world-class research facilities, including a high-performance computing (HPC) cluster with ~30,000 cores and 34 GPU nodes. Start