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Field
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support for the lab Job Requirements: Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, or related field Knowledge and experience in world model, computer vision and deep learning
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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minimum qualifications at the time of hire. PhD in computer science, data science, or related discipline Track record of publications in Artificial Intelligence and Deep Learning in peer-reviewed
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, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply
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(payable 14 times per year) Responsibilities The applicant is expected to establish an own research group with focus on advanced machine learning and deep learning techniques for remote sensing applications
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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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. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
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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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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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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