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Field
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Understanding human motivation requires methods that go beyond questionnaires and simplified computer-based tasks. This project aims to develop more naturalistic, yet highly controlled, behavioral assays in which
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completed) in Natural Language Processing or a closely related area. Solid knowledge of machine learning, especially deep learning. Experience in model development and/or fine-tuning. A practical mindset
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(adaptive) imaging strategies and reconstruction. The research combines ultrasound physics, signal processing, machine learning, computational imaging, and clinical translation. Beyond your individual
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Yip and Dr. Nikolay Atanasov (Department of Electrical and Computer Engineering), and will also collaborate with co-investigators at Stanford University, UC Santa Barbara, Vanderbilt University, UNC
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, deep reinforcement learning, and human-machine interaction. Facilitate strategic collaborations across departments within the College of Engineering and with the UM Miller School of Medicine, to advance
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custom experimental systems. These instruments may combine optical components, laser and spectroscopic methods, spin-control or magnetic-resonance techniques, electronics, data-acquisition hardware, and
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systems, from the physical fabrication of flexible printed circuit boards (FPCBs) to the implementation of machine learning algorithms for real-time signal analysis. The postdoc will collaborate in a
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networks, RNNs, LLMs) or in the deployment of such algorithms. Experience with specialized computational architectures such as GPUs, FPGAs, neuromorphic processors, or machine learning accelerators
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and developing a machine perfusion system to improve organ preservation. The candidate will work in a dynamic, multidisciplinary environment alongside PhD-level engineers and scientists, graduate
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. These instruments may combine optical components, laser and spectroscopic methods, spin-control or magnetic-resonance techniques, electronics, data-acquisition hardware, and software control. The candidate will use