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University Centre for Energy Efficient Buildings, Czech Technical University in Prague | Czech | 2 days ago
23 Oct 2025 Job Information Organisation/Company University Centre for Energy Efficient Buildings, Czech Technical University in Prague Research Field Computer science » Computer hardware Computer
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metrics. - Uso de técnicas de aprendizaje máquina/profundo y técnicas de segmentación aplicado al procesamiento de imágenes y vídeo / - Use segmentation and machine/deep learning techniques applied to image
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Position Overview School / Campus / College: College of Engineering Organization: Electrical and Computer Engineering Title: Research Assistant Professor (Non-Tenure) - Li Lab Position Details
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programming language. Does this position have supervisory responsibilities? No Preferred Education/Experience B.S. in Anthropology, Computer Science, or related fields; experience with digital mapping, GIS
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knowledge of Python Interest in AI, machine learning, or game design Ability to work independently and meet milestones Modes of Work Positions that are eligible for hybrid or mobile/remote work mode are
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tools and computer programs to review data. Assist with data cleaning measures to ensure accuracy of data and preparation of tables. Lead basic activities such as data collection and data entry. May lead
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metrics. - Uso de técnicas de aprendizaje máquina/profundo y técnicas de segmentación aplicado al procesamiento de imágenes y vídeo / - Use segmentation and machine/deep learning techniques applied to image
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de técnicas de aprendizaje automático (ML/DL) aplicado a imágenes. / Proven experience in the use of machine learning techniques (ML/DL) applied to images. Experiencia demostrable en el desarrollo de
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, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status
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making and machine learning, with real-world testing and feedback. The successful applicant will work on decision making for anomaly detection, behaviour analysis and surveillance decisions, under