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Field
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counting algorithms), and statistical physics. About You The successful applicant will have, or soon obtain, a PhD degree in mathematics or related, or equivalent level of professional qualifications and
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- Participation in research seminars and group discussions - Supervision of interns, collaboration with PhD students The team has expertise in cryptographic methods, algorithms, and complexity theory, and includes
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systems to applications in urban science, planning, and decision support. Qualifications Required: PhD in Computer Science or a related field, with a focus in databases, data systems, theory, or algorithms
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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI systems
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, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms that challenge traditional assumptions in databases, guided by
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 16 hours ago
. Description: This opportunity is closed to applicants who are Senior Fellows (5-years or more past PhD). The forthcoming Atmosphere Observing System (AOS) mission aims to provide spaceborne observations
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the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations
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will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including single-cell RNA-seq, spatial transcriptomics and