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will be an advantage if candidates have an interest in optimisation, control and computing systems, model checking, mathematical logic and good programming skills, ideally in MATLAB, Python and/or C/C
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environmental and life sciences challenges. Courses will cover topics such as complexity science, mathematical modelling in evolution, ecology and plant biology, along with transferable skills, including
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of methane dynamics in rapidly changing ecosystems and contribute to improving predictive models of future methane emissions. Field sampling will focus on regions where methane cycling is still poorly
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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are seeking a highly motivated PhD candidate to develop efficient on-device generative AI systems based on large language models (LLMs). The project focuses on creating compact, low-latency, and energy
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Cynwyd, Pennsylvania 19004, United States of America [map ] Subject Areas: Industrial Engineering Physics Economics Mathematics Management Science & Engineering (more...) Computer Science and Electrical
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an independent impact assessment of potential climate interventions in the Arctic marine environment through laboratory experiments and computer modelling. The team will develop physical, climate and ecosystem
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The project: We invite applications for a fully funded PhD studentship in the Solid Mechanics Group at the University of Bristol to work on the predictive modeling of hydrogen-induced damage in
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be