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Field
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candidate will play a key role in designing and implementing innovative solutions at the intersection of sensor data collection, machine learning, and real-time decision-making. Specifically, the candidate
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on the monitoring and response parts, building on many earlier projects revolving around the use of UAV/drones, computer vision and machine learning, change and damage detection, and multi-data integration, such as
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, interdisciplinary, and international team Successful candidate should have prior experience in at least one of those areas: analytical methods (e.g. profilometry, SEM), machine learning and/or modelling, embedded
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Description Industrial PhD: Application of artificial intelligence and data analytics to the optimization of industrial processes Where to apply E-mail doctorats.industrials.recerca@gencat.cat Requirements
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studies. Soft Skills: • Research spirit, and analytical skills; • Ability to adapt and integrate into a team; • Proactive in identifying research opportunities and proposing novel ideas for future projects
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experience with knowledge graph standards (e.g., RDF, OWL, SHACL); familiarity with GIS, geodata infrastructures and geo-analytical workflows some experience with AI and machine learning methods to label texts
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of Prof. Dr. Frank Cichos and Dr. Nico Scherf (Max Planck Institute for Human Cognition and Brain Sciences). The position is part of a collaborative project in the Center for Scalable Data Analytics and
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experience includes: Nano-imaging or sensing methods Optical or vibration detection technologies AI/machine learning for imaging and sensing Background in biology, microbiology, or biomedical sciences
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off-the-shelf sensors and the development of resilient algorithms that combine first-principles modeling with modern machine learning techniques. The goal is to push the boundaries of robust perception
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we