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the Benefits for Postdoctoral Candidates website for more information regarding benefit eligibility. Competitive wages, paid holidays, and generous time off Continuous learning opportunities through
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studying deformation mechanisms in refractory alloys as via atomic-scale calculations as well as application of machine learning to materials discovery The ideal candidate will have the following
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these documents on the application under CV/Resume. Why Texas A&M University? Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all
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machine learning tools. The postdoctoral fellow will contribute to various aspects of the project, such as: * developing new theoretical and numerical approaches for determining the thermodynamic and
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our education, while benefitting them with our talent supply and collaborative research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
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, families and domestic partners, and offers voluntary insurance that includes vision, dental, long-term disability and more. Leave: Full-time post-doctoral associates earn paid leave bi-weekly. To learn more
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Limitation:Temporary (2 years) Contract:TV-L Your tasks Develop and implement computational pipelines for processing and analyzing ONT RNA/cDNA sequencing data. Apply machine learning and signal processing approaches
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 3 hours ago
on the application of artificial intelligence and machine learning to solving complex trajectory design problems. Specific applications will focus on tour design strategies and trajectory design within other multi
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sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr. Eran Elhaik to design machine-learning models that unlock