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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 microbiome and genome
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for accountability, transparency, inclusiveness, and misinformation. The key technology is multimodal deep learning, and its extensions for these additional targets. In particular, we have a large collection of
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theoretical advances into practical insight and tools that can support analysis, design and decision-making for AI-enabled space systems, thereby bridging the emerging scientific theory of deep learning with
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch
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, particularly artificial neural networks, and deep learning? And you would like to continue your research on clinically relevant topics? If yes, then Maastricht University has a new challenge for you! Postdoc
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progression and treatment response, and support personalised AF management. The postdoctoral researcher will develop, implement and validate state-of-the-art machine learning methods, ranging from deep learning
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; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and
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and authorities interested in improving civic participation. The research in this postdoctoral position focuses on text and image feature learning for news ecosystems, analysing the complex
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(e.g. use of OpenRouter, vLLM). Experience training LLMs and deep learning in general is a plus; good communication skills in oral and written English. Contributions to OpenML or other dissemination
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and