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Field
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“algorithmic bias” in AI systems; understanding what it would be to “align” AI systems with ethical norms; developing and evaluating proposals for the governance of powerful AI systems; the ethical issues raised
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to achieve scalability in terms of the simulator systems. The work will be done in close collaboration with the physics team to be able to develop optimizations also at the algorithmic level in a co-design way
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to make a difference. Do you want to be involved and contribute to our development? Together, we can create a sustainable future through knowledge and innovation. We believe that knowledge and new
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, scalable numerical algorithms for extracting the evolution model of the relevant dynamical skeleton, quantifying associated uncertainties. We will develop mathematical theory underpinning the novel
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using deep learning, computational chemistry, medicinal chemistry, chemical biology, and molecular cell biology to develop novel therapeutics to tackle complex diseases such as cancers. Successful
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research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic disease. Key
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between the University of Plymouth and Cornwall Partnership NHS Foundation Trust, starting in May 2026. About the role The purpose of the role is to develop and apply mathematical models and computational
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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Science and Engineering, or a related area is required. The position will involve developing models and algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research
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managing supercomputer resources Strong skills in algorithm development for large sparse matrices Excellency in programming GPU accelerators from all major vendors Very good command of written and spoken