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or junior graduate students. A formal training, education, or certification in a secondary area (beyond the main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital
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, reinforcement learning, probabilistic modeling, and language-guided autonomy. Core Responsibilities: Conduct independent and collaborative research aligned with the themes above Mentor PhD and MS students Lead
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Experience A PhD in Physics, Astronomy, Computer Science, or a closely related field is required. Experience with HPC systems, jax, python, or similar programming languages, machine learning, and transient/AGN
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enthusiastic scientist with the following competencies and experience: Essential experience and skills: You have a PhD in Machine Learning, Artificial Intelligence, Bioinformatics, Biostatistics, Epidemiology
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data from existing cohorts and national registries, applying novel machine learning methods. The specific work tasks will include data management of large studies, scientific work related to the topics
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 5 hours ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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Aalto University is inviting applications for a Postdoctoral researcher in molecular machine learning. The successful applicant will join the research group of Professor Juho Rousu. The position
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI