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🌍 PhD Opportunities in the U.S. (Fully Funded) | Zynthera in Partnership with NYU & UCLAZynthera is a research-driven, interdisciplinary science and innovation company headquartered in Rome
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, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will
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focus will be on developing compact, efficient, and real-time LLM algorithms/hardware on the edge and developing demos for specific applications such as speech disorder therapy. Your responsibilities will
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demonstrated experimental realizations and proven theoretical advantages. The project may involve several aspects, including mathematical theory, algorithm development, error correction, adaptation of GBS-based
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Approximation. Parameterized Complexity is a vastly growing area within theoretical computer science that allows for the development of exact and approximation algorithms for computationally hard problems by
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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling Job No.: 683222 Location: Clayton campus Employment Type: Full-time Duration: 3.5 to 4-year fixed
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the evidence–base needed to understand the impact on health, to inform public policy, and to develop potential mitigation strategies. Traditionally, this information has come from ground monitoring networks
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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candidates will undertake a minimum of 60 FTE business days of research and development with the organisation. The AGRS will support eligible students with a research internship scholarship (for 60 business
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms