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Field
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reconstruction and processing. Candidates are expected to have a strong background in medical imaging, experience in imaging system evaluation, deep learning and clinical investigation. Brief Description of Duties
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applicants who combine strong quantitative skills with a deep interest in the fundamental mechanisms shaping plant communities and their response to climate change. Qualifications: • PhD in Ecology or a
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You should have a PhD or equivalent qualification/experience in Physics or Engineering, in a field related to this project. Training in unique techniques will be provided, but we expect you to have
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assisting with in-situ TEM measurements, facilitating cutting-edge research in sustainability and energy fields. Part of the project will also include the development of deep learning frameworks for TEM image
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serving (Ray/VLLM), quantization and sharding, prompt optimization, reinforcement learning, Transformers/Deep-SSMs/Test-Time Regression Extensive knowledge of agentic AI systems research, engineering and
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environments (Gazebo, Unreal Engine, or Unity). You have experience in artificial intelligence (Deep Learning, PyTorch) or embedded systems (ROS2, FPGA/VHDL design). You are curious, show scientific rigor and
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highly stimulating environment that engages the best and brightest faculty and students to conduct deep and impactful research. Our faculty's research expertise and strengths cover several key
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. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering, telecommunications or related field. Other requirements include: Strong background in wireless communication systems and
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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Relativity, and cosmological measurements using GWs such as Hubble constant and probes of inflation and phase transitions in the early universe. We are developing new data analysis methods like the use of deep