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(particularly computer vision), (2) machine learning for data analysis, (3) sensor technologies (e.g., electromagnetic sensors), (4) design and integration of mechanical/electrical devices; and an interest in
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Glimpse of the Job The Postdoctoral Research Associate will perform independent research activities requiring an advanced level of knowledge in a field of science or learning. This work is predominantly
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research activities across disciplines including aerospace, combustion, design, fluid mechanics, materials, mechanical, mechatronic and robotics engineering. To learn more about the School click here We
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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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responsibilities of this position: (40%) Research: Measure eco-physiological responses of insects to cold exposure. Work includes contributions to experimental design, quality control/quality assurance, recording
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conditions, helping to improve design, control, performance, and reliability. What you would be doing: You will focus on developing nonlinear dynamic models of fuel cell electrochemistry and on identifying
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for individuals, organizations or society. You will be part of the SAIL research group, whose research activities focus on AI-based modeling of complex systems, uncertainty management in deep learning, visual
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design within an active, growing translational research program. This is an unusually opportune time to join the lab: the IBC work sits at the leading edge of a paradigm shift in how recurrent UTI is
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learning models (e.g., multimodal AI, large language/world models) with specific finetuning for ELEVATE; designing geographically context-sensitive urban design recommendations that promote active mobility
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outputs. The research methods will include experiment design, software implementation, running deep learning experiments on high-performance computing infrastructure, and evaluation using objective metrics