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evaluate and embed learnings from the project that will influence T Level policy and curriculum development. The project has workstreams around the promotion of technical careers, T Level placements, and
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Qualifications The following qualifications and experience will be considered an advantage: Experience with crop modeling. Experience with plant breeding. Background in data science, machine learning, and
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, condensed matter physics, or a related field, with experience in first-principles calculations and/or machine learning for materials research being highly desirable. For additional information about this
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an enthusiastic and highly motivated researcher. You have, or will shortly, acquire a PhD degree in the field of Human-Computer Interaction, Interaction Design or Interaction Technology or equivalent Ideally, you
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completed) in Natural Language Processing or a closely related area. Solid knowledge of machine learning, especially deep learning. Experience in model development and/or fine-tuning. A practical mindset
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at the interface of automatic control, electrochemistry, and machine learning. The position will also involve close collaboration with another postdoctoral researcher working on a complementary project in physics
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Postdoc (f/m/d) Mechanical Characterization and Materials Assessment for Nuclear Applications / P...
data analysis and statistical methods # Interest in or initial experience with data-driven methods or machine learning is an advantage # Strong communication skills and a collaborative approach to
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independently and in a team A focus of research in one of the following areas is desirable: Language ideologies Literacy development Digital practices Coding and/or machine learning We further expect you to have
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time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https://resources.uta.edu/hr/services/records/compensation-tools.php CBC
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packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process adaptive and scalable. The research effort will be directed towards the selective recovery in