372 parallel-and-distributed-computing "Computer Vision Center" PhD positions in United Kingdom
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Low vision and vision impairment School of Allied Health Professions, Pharmacy, Nursing and Midwifery PhD Research Project Self Funded Dr Gemma Arblaster , Dr S Toor , Dr C Codina Application
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Nonlinear Distributed Control School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Pablo Baldivieso Monasterios Application Deadline: Applications accepted all year
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with the ARIA Safeguarded AI programme project “Data-Parallel Proof Checking for Monoidal Theories”, led by Professor Fabio Zanasi within the Programming Principles, Logic and Verification (PPLV
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Quantum computing for solving differential equations and data-driven scientific computing problems (S3.5-MPS-Kyriienko) School of Mathematical and Physical Sciences PhD Research Project Competition
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rapid development of artificial intelligence (AI) has created an increasing demand for computing hardware capable of processing complex information with high speed and low energy consumption
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- Custom Computing Performance Benchmarking Job description About the job: Play a key role within QUB's strategic Centre for Secure Information Technology (CSIT) and engage in challenging research projects
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(GIST) (aka HCI) Information, Data and Analysis (IDA) GLAsgow SyStems (GLASS) Education and Practice Research themes Cyber security Healthcare technologies Low carbon and sustainable computing Media and
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Physics-Informed Symbolic Distillation for Domain-Specific Vision Applications (S3.5-COM-dos Santos)
Physics-Informed Symbolic Distillation for Domain-Specific Vision Applications (S3.5-COM-dos Santos) School of Computer Science PhD Research Project Competition Funded Students Worldwide Dr
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room situational awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in
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emulation tools combined with profiling frameworks for energy, latency, and throughput. Benchmark across representative workloads (e.g., TinyML, edge vision, federated learning). The successful candidates