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Field
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Join a dynamic team of motivated individuals with deep collective experience throughout digital forensics, incident response, investigation, operations, and academic research. We seek individuals
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Join a dynamic team of motivated individuals with deep collective experience throughout digital forensics, incident response, investigation, operations, and academic research. We seek individuals
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Computational Imaging for High-Throughput Applications (1.0 fte) As a PhD student, you will be embedded in the research group Computational Imaging and Deep Learning (CIDL), part of the Leiden Institute
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PhD candidate who shares our deep interest in exploring conceptual, educational, social, and governance issues that emerge in the context of scientific modelling. The candidate also brings the following
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) in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related quantitative field. Further qualifications: Technical Skills: Advanced proficiency in Python and deep
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
. The successful candidate will be expected to carry out the following scientific activities: Application of advanced Machine Learning, Deep Learning, reduced-order modelling, and physics-informed/-guided modelling
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and familiarity with at least one deep learning framework Basic understanding of NLP concepts (e.g., language models, tokenization, fine-tuning) Strong attention to detail, ability to follow
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of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will
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actively on the preparation and defence of a PhD thesis in the field of explainable reinforcement learning (XRL). Explainable reinforcement learning aims to make decisions, policies, and learning processes
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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks