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Field
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in image processing and analysis, including deep learning (e.g., CNNs) experience with correlative imaging workflows and 2D/3D registration techniques strong programming skills in Python and/or C/C
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venuesStrong programming skillsSolid mathematical foundation, including linear algebra, probability, statistics, and optimizationBroad and in-depth experience with machine learning algorithms and deep learning
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mathematical 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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), Kinetic Modeling. Machine Learning Interatomic Potential (MLIP) accelerated simulations. Demonstrated ability of coding in Fortran, Shell, or Python with development experiences. Deep knowledge in excited
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, clinical trial of behavioral treatment for children with obesity and their families. This position provides an outstanding opportunity to build unique and deep experience and expertise in designing
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA | North Ryde, New South Wales | Australia | about 1 month ago
experience in: Radial velocity, stellar, and/or exoplanet spectroscopy studies; and/or astronomical instrumentation or observing programmes Machine learning in physical science, using Jax, PyTorch, and/or
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of operating underground with minimal human intervention. By joining this project, you will strengthen your scientific profile while gaining deep hands-on experience in mobile manipulation, contact-rich robotics
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physically and functionally coupled across domains of life. The project will involve working with syntrophic deep-sea consortia, performing strictly anoxic physiological experiments, and developing electrode
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imaging, deep proteomics, metabolomics, metaproteomics, and machine learning (ML) approaches to develop diagnostic classifiers, spatial tissue atlases, and identify potential therapeutic targets