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strategies. Duties and Responsibilities Design, implement, and evaluate deep learning models for spatiotemporal data, with an emphasis on medium-scale foundation models. Leverage model embeddings in causal
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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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testing methodologies for physical modelling of deep-water offshore wind turbines. You should hold a relevant PhD (or be near completion) and have a strong publication history. A strong background in
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. ESSENTIAL REQUIREMENTS A PhD inMachine Learning, Computer Vision, Computer Science, Physics, Engineering, Mathematics or related areas. Documented expertise in: Machine/Deep Learning, and possibly Computer
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for embedded and GPU platforms. Collaborate with ARSPECTRA engineers and surgeons to create a complete AR guidance pipeline : tracking, SLAM, gaze, user interface Your profile PhD in machine learning
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decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model
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the measurement instrument in close collaboration with our industrial partner, Veridis Technologies. An ideal candidate has experience in vibrational spectroscopy and spectral processing. Expertise in deep learning
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development, and publication in peer-reviewed venues. Strong background in machine learning, with research experience in deep learning, foundation models, or related areas. Solid programming ability in Python
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Computer Science, AI, Machine Learning, Computer Engineer or a related field. Strong background in machine learning and machine learning frameworks Proficiency in Python and deep learning frameworks A combination of
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. Experience in high-throughput sequencing data analysis and cluster/cloud computing. Proficiency in variant calling, single-cell DNA and/or RNA analysis, and machine/deep learning (preferred but not required