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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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, 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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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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As a postdoctoral researcher, your primary responsibilities will be: Develop machine learning and deep learning models, with a strong focus on computer vision, for the characterisation and
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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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; 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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deep learning, and its extensions for these additional targets. In particular, we have a large collection of newspaper articles dealing with migration-related topics, and we are investigating how text
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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
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at the atomic scale, using density functional theory-accurate machine-learned potentials and molecular dynamics simulations, in close collaboration with leading European research institutes and steel industry
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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