38 parallel-and-distributed-computing-"UNIS" Postdoctoral positions at Northeastern University
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institute and would be encouraged to find ways to thrive and grow their own research program. This position is Boston-based and is available through grant funding that runs until August 31, 2026
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adversarial AI and out-of-distribution detection. The successful candidate will have a strong track record in top-tier AI publications, outstanding presentation and leadership skills. Responsibilities
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About the Opportunity SUMMARY Northeastern University invites applications from outstanding candidates to fill one or more Postdoctoral Research Associate (PRA) positions in computational quantum
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programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or large-scale data centers
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for large-scale and distributed systems. The role focuses on developing and analyzing algorithms, building reproducible computational studies, and disseminating results in leading venues. The position
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About the Opportunity The Postdoctoral Research Associate advances research in mathematical modeling, optimization, and stochastic analysis for large-scale and distributed systems. The role focuses
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behavior, human-computer interaction, psychology, computer science or related disciplines to work on research projects on human-AI interaction. Increasing ability to generate human-AI interaction data needs
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proposal development and preparation of high-quality publications in top computer security, privacy, embedded systems, sensing, and networking venues. ● Pursue research topics such as protecting
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Health based at The Walter and Eliza Hall Institute in Melbourne, Australia. You will be part of a multi-disciplinary team that has an ambitious long term research program to decipher what factors result
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research. Prepare and write research papers, presentations, grant proposals. Train Graduate Students. Qualifications: PhD in Computer Science or related field (completed or near completion) Strong