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Field
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interdisciplinary and experiential learning to join the Source Project , Binghamton University's distinctive first-year research program in the Humanities and Social Sciences. The position begins August, 2026. PhD in
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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industrial component given the close collaboration with the quantum computing start-up, Quantum Motion, particularly with Dr. Ciriano-Tejel, machine learning group. Key responsibilities Conduct research
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++, Python, and JavaScript languages, multi- and many-core SoC, RISC-V, hardware synthesis, hardware-software co-design, (meta-heuristic) optimization algorithms, machine learning frameworks, (bonus topics
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perform routine logging and/or testing of samples. May occasionally instruct others in basic laboratory techniques. Working Conditions: Work is performed on-site in Cambridge, MA. May be required to work
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associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build healthier, more educated
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-stage researchers in advanced data analytics, causal inference and machine learning related to health policy topics. Specifically, it is training them to evaluate real-world policy impacts. Focusing
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conferences and workshops. The Research Assistant Professor may be asked to teach (or may ask to teach) but that is neither required nor guaranteed. Qualifications Applicants must have a Ph.D. in Economics or
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for this position, however, we are still accepting applications from other qualified candidates. Position Summary Our goal is to identify computational principles underlying learning and decision-making behavior and
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experience in Artificial Intelligence (AI) and Machine Learning (ML) concepts, algorithms, and frameworks; Hands-on experience with popular ML libraries and tools (e.g., TensorFlow, PyTorch, scikit-learn