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aims to develop new foundations and technologies for reasoning and planning that leverage large language models (LLMs) and integrate them with complementary approaches, including program synthesis, and
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implicated genes act in neurons and circuits. We use large scale, unbiased, systematic approaches in collaborative multidisciplinary research teams. This postdoctoral fellowship in the Arlotta lab involves
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) to uncover patterns and mechanisms of animal influences on ecosystem properties and functioning. The Davies Lab have used unoccupied aerial vehicles to collect a large amount of LiDAR data spanning natural and
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preferred, and this can take various forms: working with large model simulations or large data, compiled programming languages, algorithms, etc. The position is an especially good match for candidates
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Science and Engineering Position Description The successful candidate will work on using geostationary satellite observations to detect and quantify large atmospheric methane releases. They will also have
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-performing big data systems. Topics of interest include machine learning systems such as neural network systems (training and inference) and storage systems for AI. Examples of our group’s past work on these
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experience, will work as part of a larger team to assist with collecting and analyzing data gathered from human subjects, both in field, clinic and lab studies as part of evaluations of the technology. A large
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with collecting and analyzing data gathered from human subjects, both in field, clinic and lab studies as part of evaluations of the technology. A large part of the role will focus on supporting a
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research on performance and value creation outcomes from AI, digital, and big-data driven transformation in organizations. An ideal candidate will be interested in research on digital transformation and AI
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machine learning methods for computational materials physics and chemistry. Projects include: 1. Scientific software engineering of machine learning potentials for large scale molecular dynamics. We