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causal analysis across distributed datasets while preserving privacy. The successful candidate will be responsible for the end-to-end investigation of novel federated learning strategies for causal
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Professor will be responsible for teaching and research in some of these areas: computer architecture, media, database, algorithms, parallel and distributed systems, etc. Only shortlisted candidates will be
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sites (k-nearest neighbor algorithm, centroid models, distribution models, etc). We are also expanding on our previous work applying community detection methods, such as modularity maximization and
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Ph.D. degrees. The Department has research strengths in data mining, databases, graphics, image and video computing, machine learning, natural language processing, networking, distributed systems
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experimental facilities. You will be responsible for developing energy-efficient, physics-aware algorithms designed for distributed learning across both high-performance and edge computing environments. You will
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for department donors • Design a quarterly newsletter for distribution Event Planning • Assist senior staff in event planning for seminars, faculty meetings, DH meetings, etc. • Schedule hotels/conference space
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organizing/publishing software packages. Familiarity with core data structures, algorithms, and some exposure to parallel or distributed computing. Strong quantitative and critical-thinking abilities
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focus on a variety of key technologies like Distributed IT Systems, Internet of Things, IoT, Cybersecurity, Data Science, Artificial Intelligence (AI), Blockchain Technologies, Quantum & Photonic
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focus on a variety of key technologies like Distributed IT Systems, Internet of Things, IoT, Cybersecurity, Data Science, Artificial Intelligence (AI), Blockchain Technologies, Quantum & Photonic
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software packages. Familiarity with core data structures, algorithms, and some exposure to parallel or distributed computing. Strong quantitative and critical-thinking abilities demonstrated through