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patients, generating new clinical multi-omics data and using deep learning, structural equation models, and causal inference to identify strategies for restoring immunological homeostasis. Together
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science, artificial intelligence, or a closely related field. A strong background in machine learning and deep learning. A good understanding of Transformer architectures and modern AI models. Good programming skills
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to the group’s open-source software, and participation in the supervision of students. A limited amount of teaching may be included (max 20%). Requirements PhD degree in machine learning, computer science
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archaeological signatures (e.g., micro-relief, edge structures, etc.) – Design and implementation of new deep learning architectures (both supervised and unsupervised/few-shot, 2D and 3D) for an efficient and
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function based on a coupled NEMS network, consisting of 2 or more double-drum resonators. This is beyond current state of art and relies on deep understand of more degrees of nonlinear complexity
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at least one deep-learning framework (PyTorch preferred).•A solid grounding in machine learning. Experience with representation learning, generative models, foundation models or multimodal integration is a
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LevelMaster Degree or equivalent Skills/Qualifications Solid background in Machine Learning and Deep Learning. Experience or interest in agentic AI frameworks (e.g., LangChain, LangGraph, AutoGen, or similar
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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or
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missions operated by LATMOS. The postdoc will employ deep learning approaches using satellite data and ground stations. -Understanding the infrared data from the IASI mission and identifying the channels
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on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and Data Science, Safety and