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Field
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data from measurement and observation networks, as well as snow testing protocols, to provide access to a larger, harmonized information system on both sides of the border; Mutual training and
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is currently building and commissioning a network of calibrated multi-sensor observatory-class systems, and developing novel machine learning methods with the aim of collecting science-quality data
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-qualified biosensors for detecting molecular biosignatures during planetary exploration missions. The LMCOOL system uses integrated photonic circuits with asymmetric Mach-Zehnder Interferometer (aMZI) sensors
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are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we
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Interferometer (aMZI) sensors on silicon nitride platforms, enabling highly sensitive, label-free detection of biomolecules for astrobiological research. An essential component of the technology is chip
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sustainable development. With state-of-the-art infrastructure, UM6P has built a strong academic and research network and seeks to recruit high-quality professionals to enhance its research- focused environment
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. To reach level-4/5 autonomy, we need teamwork: nearby vehicles, drones, and roadside units must co-perceive their environment, sharing and fusing complementary sensor views in real time. Yet raw video
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grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations
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motivated postdoctoral fellow to investigate how cell death sensors regulate immune responses, with an emerging interest in the retina. Building on our prior work (PMID: 35969037, PMID: 36662852) we aim
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data, including TPMS (Tire Pressure Monitoring System), high-speed wheel encoders, CAN (Controller Area Network) data, accelerometers, and acoustics data. Understanding of sensor technologies and their