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Field
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to collaborate closely with our Danish industrial partners on innovative use cases, including advanced condition-based monitoring in industrial settings and speech enhancement in audio headsets. The appointed
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data collection approaches. Familiarity with or strong motivation to learn machine learning or advanced data analytics for pattern detection and forecasting in environmental data. Familiarity with
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, disability, ethnicity, familial status, gender (including transgender), gender identity or expression, genetic information, HIV/AIDs status, military status, national origin, pregnancy (false pregnancy
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. Responsibilities include working with digital signal processing, advanced filtering techniques, dynamic feature extraction, time-domain and frequency-domain analysis, signal fusion and machine learning to enhance
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records from satellite data, and/or improved methods of uncertainty characterisation, including the use of artificial intelligence and machine learning to improve or analyse satellite climate data records
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Monitoring (LTVEM) in the hospital for management and diagnosis of epilepsy. The technology is built on brain computer interfaces equipped with a Spiking Neural Network (SNN) and aims at early detection
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Biomedical Engineering, Medical Physics, Electrical, Computer Engineering or a
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), sexual orientation, or military status. Duke aspires to create a community built on collaboration, innovation, creativity, and belonging. Our collective success depends on the robust exchange of ideas
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for healthcare. The Alsentzer Lab is an interdisciplinary research group in the Department of Biomedical Data Science at Stanford University. Our mission is to leverage machine learning (ML) and natural
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman