435 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" scholarships
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Field
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established classical protection schemes with data-driven methods, including artificial intelligence and machine learning. The proposed protection strategies are expected to exhibit the following key attributes
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work with the UK semiconductor industry. The studentship represent a unique opportunity to be trained in the epitaxy process and to work in an emerging and exciting area of combining AI/machine learning
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 22 days ago
applicant has a strong background in bioinformatics and/or probabilistic machine learning, as well as experience in omics data analysis, and possesses solid English-language skills. Experience with
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critical maritime operation or system Collecting and curating operational and security-related data for AI-based threat analysis Training AI and machine learning models for anomaly and threat detection
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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Develop new machine learning
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on advanced machine learning and emulation approaches. Key responsibilities: The candidates will be expected to work on the following tasks: - Develop machine learning (ML) methodologies appropriate
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solutions across the natural sciences. Your workplace You will be employed at the Department of Mathematics in the Division of Applied Mathematics, https://liu.se/en/organisation/liu/mai/tima . The research
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different backgrounds. This position requires that you have graduated at Master’s level in in computer science, media technology, computer engineering, human-computer interaction, visual learning and
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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
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), the sorption of PFAS and heavy metals onto natural nanoparticles will be investigated in situ using a dedicated field exposure method developed by our team, complemented by laboratory experiments and machine