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, please contact Dr. Maxime Cordy Your profile Master in Computer Science. Major in machine learning or software engineering is an asset Strong programming skills Strong analytical skills Industry experience
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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
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learning, single-cell genomics, and neurogenetics. Your project will focus on developing new methods to understand how genetic variants affect not only individual brain cells but also change the way cells
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protocols, ITC will focus 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
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are seeking a motivated PhD student to join our team working on realizing learning in novel physical materials, as part of a joint theoretical/experimental research project between AMOLF and the University
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analytical chemistry, mass spectrometry, or trace gas measurements is an asset Enthusiasm for laboratory and field work Willingness to engage in programming and data analysis (preferably Python) Excellent
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, the selected researchers will deal with: Research & Development: Designing, developing, and implementing state-of-the-art machine vision and deep learning algorithms to analyze complex image and sensor data
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your hard and soft skills, and involves learning-on-the-job, following dedicated courses, and of contributing to the group’s educational activities. The aims of your work are to develop innovative
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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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, Psychology, Evolutionary Anthropology, Philosophy, or similar), near native-level of spoken and written English, and robust conceptual and analytical skills, as documented through course work or research