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Field
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tools that help to solve an environmental challenge Growing as a scholar (20%) Working closely with our team of data scientists and software engineers and finding ways to both learn from their approaches
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combine ophthalmological, neuroimaging and behavioral data, and incorporate deep learning methods to facilitate biomarker discovery and enhance predictive power. As a postdoctoral associate you will join a
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SIT's mission is centred on nurturing industry-ready graduates who possess deep technical expertise and transferable skills to address future challenges. We collaborate with industry in our
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background in grid-connected power converters. · Deep knowledge of advanced power conversion topologies, particularly MMC and SST. · Proficient in simulation tools for power converters, such as
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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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cutting edge technologies in robotics and AI (Autonomous Robots, SLAM, Navigation, Perception, Human-Brain Interface, Deep Learning and their Security, etc.). CAIR has state of the art autonomous unmanned
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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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critical deep tech innovations that impact our national security in the clean energy, biotech, advanced materials, Al and advanced computing, and space sectors. Entrepreneurs from across the country
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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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communication and collaborative skills. Experience with SLAM, sensor fusion, LiDAR/depth camera data processing. Familiarity with deep learning for obstacle avoidance (e.g., map-less navigation). Background in