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equipment. You will ensure sensors are calibrated and will implement, improve, and interpret data quality assurance algorithms. You will also produce reports as needed for distribution to agricultural
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algorithms, like multi-camera approaches or sensor fusion. You will implement currently available methods in this area, evaluate their performance and systematically study their dependability. Onsite
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, characterisation and/or calibration of image sensors. Good understanding of, and demonstrable experience in, writing bespoke data acquisition and data analysis code and/or software algorithms with a focus on
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will work in a group with other students and take on specific tasks. The aim is to analyse the robot's capabilities and to implement algorithms that enable the robot to be used sensibly in applications
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of Biomedical Engineering, please visit https://www.bme.ubc.ca/ . HuMBL, a research lab within the SBME, specializes specifically in wearable sensor technologies and algorithm-driven health analytics to enhance
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
pipeline into production, working with a team of scientists to develop, implement, and deploy cutting edge algorithms and approaches to astronomical difference image analysis, image resampling and
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and simple embedding alignment to develop architectures that can process and reason across modalities (vision, language, audio, sensor data) from the ground up. How do we build a truly unified
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sensor data) mentioned. * Experience with multi-omics data analysis (e.g., genomics, transcriptomics, proteomics). * Familiarity with neuroimaging data analysis tools (e.g., FSL, SPM, or FreeSurfer
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and interoperability, and efficient processing and management of time-series datasets and metadata originating from IoT sources (such as environment sensors and meters) closer to the data provider
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. clinical data, imaging data, genomic and omics data, device and sensor data) mentioned. ● Experience with multi-omics data analysis (e.g., genomics, transcriptomics, proteomics). ● Familiarity with