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learning, signal processing, computer science, or a related field. Strong experience with deep learning and generative models. Programming skills suitable for modern machine learning research, preferably
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, watermarking, source tracing, and generative AI. Good programming skills, preferably including Python and modern machine learning tools. Basic knowledge of deep learning, signal processing, speech/audio
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collection, data engineering, statistical modeling, or computational text and image analysis; Machine learning, natural-language processing, large language models, or evaluation and auditing of AI and online
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
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, computer vision, robotics, biomedical engineering, computer science, biomechanics, neuroscience, signal processing, or a closely related discipline. Strong expertise in machine learning and deep learning
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environments; (2) identify which patterns of student-AI interactions influence the adoption of deep or surface approaches to learning; (3) create, implement and evaluate guidelines and knowledge base
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a sequential decision-making process explored through computational simulation and deep multi-objective reinforcement learning. The project will investigate a simulation platform that reproduces
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prospection with the FLAIR's strengths in developing new multi-objective deep reinforcement learning algorithms, to support decision makers under uncertainty. VUB team:Prof. dr. Ralf Vandam (AMGC), Prof. dr
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intelligence, data science, medical physics, neuroimaging, bioengineering, or related disciplines, accompanied by accredited training in machine learning, deep learning, or medical image analysis. Experience: A