Michigan Engineering | University of Michigan Could human-robot collaboration revitalize an outmoded business model and attract new workers?
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Remodeling the construction industry
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Resistance is not futile: Predicting how changes in production, materials impact EV battery life
As battery makers race to keep up with demand, a quick and inexpensive step can save money and time in development.
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Three members of ECE will represent U-M at the 2021 Rising Stars in EECS Workshop
PhD students Sijia Geng, Bahareh Hadidian, and Nasimeh Heydaribeni will participate in the intensive workshop that brings together outstanding women and gender minorities interested in pursuing academic careers in EECS.
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Intersection of environmental justice and engineering the focus of new $2.5M endowed professorship
The gift represents a cross-discipline approach to sustainability and equity.
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Michigan Engineering group creates model for layering COVID-19 defenses
The model can be used as a tool to inform decision-makers and individuals on relative risks and advantages associated with a layered defense.
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Energy from waste: $6.8 million for cow-inspired biodigesters
University of Michigan leads a collaboration of academic, municipal and private institutions to advance a renewable methane ecosystem.
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$1.7M to build everyday exoskeletons to assist with lifting, walking and climbing stairs
The modular exoskeleton system will help workers and the elderly, boosting ankle, knee and/or hip joints by mounting new motors to off-the-shelf orthotics.
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$1.8M to develop room temperature, controllable quantum nanomaterials
The project could pave the way for compact quantum computing and communications as well as efficient UV lamps for sterilization and air purification.
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Video: 100% renewable diesel cars can reduce carbon emissions while waiting for electric vehicles
Researchers estimate that there was an 80% carbon footprint reduction when using renewable hydrocarbon biofuels instead of traditional petroleum-sourced fuels
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Augmented reality for testing nuclear components
A new machine learning platform detects and quantifies radiation-induced defects instantaneously and could be extended to interpret other microscopy data.
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