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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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developing deep learning approaches for genome interpretation; development of methods for multi-omic and spatial data analysis and integration with phenotypic and clinical data; and machine learning and AI
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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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learning with a focus on medical imaging, and programming with one or more of the major deep learning packages (PyTorch, TensorFlow, Caffe, etc.) is desired. Application Process Please upload the following
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developing deep learning approaches for genome interpretation; development of methods for multi-omic and spatial data analysis and integration with phenotypic and clinical data; and machine learning and AI
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depends on the background of a suitable candidate. The main topics of the group in the past few years were generative modeling, 3D reconstruction, image-editing, and deep learning using 3D data. More
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learning applications involving images using Python, C++, OpenCV, Matlab and other related frameworks. Experience with state-of-the-art AI systems such as deep learning and convolutional neural networks
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processing, artificial intelligence, cognition and deep learning, machine learning, navigation and mapping, autonomous driving, assistive robotics, drones, dynamics and vibration, acoustics, medical imaging
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. The projects may also include to tackle benchmarking problems such as SAT, image processing, graph theories, boson/fermion sampling by applying classical machine/deep learning, neural network techniques and