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of biosignals features and processing techniques. Experience in designing Spiking Neural Networks (SNNs) or deep learning algorithms Preferably, experience with FPGA development of SNNs ASIC design experience is
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unravel the complex relationships between land use changes and fire regimes over the past 60 years. The successful candidate will lead efforts to: Develop advanced deep learning algorithms for classifying
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foundation and programming skills in languages such as Python, Java, Julia, MATLAB, R, and/or C++. Expertise in one or more of the following: machine learning, deep learning, HPC, Docker/Singularity
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ability to analyze large datasets Knowledge of coastal and nearshore processes Preferred Qualifications: Proficiency in statistical modeling and time series analysis Experience with machine learning or deep
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), tribology, friction stir welding/processing, and/or mechanical behavior of materials. Please attach a cover letter, resume, and list of three professional references (include name, mailing and email address
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Basis Salary More Information: Please visit “Why Carnegie Mellon ” to learn more about becoming part of an institution inspiring innovations that change the world. Click here to view a listing of
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with electronic health record (EHR) and/or clinical data. Proficiency in Python, with strong coding and debugging skills. Experience with deep learning frameworks such as PyTorch, JAX, TensorFlow
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skills. Excellent programming skills in Python and Julia with experience with deep learning frameworks (e.g., PyTorch, TensorFlow). Experience building complex software systems, preferably with industry
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workflows in complex organizational settings. Qualifications: Applicants must have a PhD in Computer Science or related field. Experience in one or more ML domains, such as deep learning, reinforcement
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 2 months ago
-on experience that provides an understanding of the mission, operations, and culture of the DOE. As a result, fellows will gain deep insight into the federal government's role in the creation and implementation