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Field
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development and application of deep learning methods, with a strong interest in understanding molecular mechanisms of disease. The position will be highly collaborative working with a diverse group of
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you will extend this work to the ultrasonic sounds of bats, insects and other animals. This involves curating open datasets of sound, as well as training deep learning, and validating that these methods
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AI hardware to help solve significant real-world problems using machine learning and deep learning. ALCF researchers work in a highly collaborative environment involving science application teams
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that provides an understanding of the mission, operations, and culture of DOE. As a result, fellows will gain deep insight into the federal government's role in the creation and implementation of energy
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equipment for tribology and failure analysis. Deep technical and functional understanding of contact mechanics and tribology. Demonstrated experience developing models to explain experimental results
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moves. Success will be measured by having published or contributed to papers in top venues (e.g., Nature Science of Learning, Computers and Education, ACM Learning at Scale, Educational Data Mining) and
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. · Publish manuscripts reporting the project’s progress and innovations. Applicants must have a PhD by the position start date. The applicant should be an expert in Python programming and deep learning APIs
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of multiple robots to achieve common objectives in dynamic and prior-unknown environments, including kinodynamic-constrained motion planning, dynamic and kinematic decoupling and transfer/deep learning-based
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primary role will be to contribute to project tasks that demand a deep understanding of electrical or thermal energy system operation and dynamics, modelling approaches, and optimization methods. To excel
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interdisciplinary thinking • High level of initiative and commitment • Ability to work in a team and communication skills • Good knowledge of Ansys Fluent • Experience with deep learning methods and packages