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University of California, San Francisco | San Francisco, California | United States | about 1 month ago
searching and integration. Maintains the computational infrastructure and tracks the flow of samples and information for large-scale studies. Provides web-based bioinformatics and access to public and
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technologies such as IoT, big data, analytics, computer vision, cloud computing, and artificial intelligence (AI). IoT devices help in data collection. Sensors plugged in tractors and trucks as well as in fields
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modelling and machine learning for large and complex datasets. Have proficiency in Python and/or R for time-series and sensor data analysis. Have an interest in or experience in environmental exposure
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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therapeutics, wearables, artificial intelligence (AI) and machine learning (ML), public health surveillance systems, and virtual/augmented/extended reality. Health conditions of interest are also broad and may
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. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time. Your qualifications You have graduated at Master’s level in Mathematics, Computer/Data
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26 Feb 2026 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions Application
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, Jose Landivar-Scott, Nick Duffield, Kevin Nowka, Jinha Jung, Anjin Chang, Kiju Lee, Lei Zhao, Mahendra Bhandari, Unmanned aerial system and machine learning driven Digital-Twin framework for in-season
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. Describe a deep learning project you have executed—ideally a creative use of a vision transformer, U-Net architecture, or Diffusion model that you trained yourself. Projects in computer vision for microscopy
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large research team. Job Description Primary Duties & Responsibilities: Designs, develops, and implements: Algorithms and computer software for omics-based data sets [high-throughput, massively parallel