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Field
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working atmosphere flexible and family-friendly working time model and the possibility of mobile working (up to 50% of working time) subsidy for a company ticket for public transport
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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an exceptional international team with expertise in all aspects of the project. Your tasks will include: • Preparation of different EO and in-situ datasets for training a machine learning model • Development of ML
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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undertake comprehensive literature and market surveys, develop advanced simulation models, investigate integration into HVDC transmission systems, and design/test scaled-down hardware models at the ‘Wolfson
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for clinical use. Generative and Predictive AI for Clinical Decision Support and Statistical Inference Develop biologically informed statistical methods and uncertainty estimation models to train deep learning
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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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of biogeochemical processes with an emphasis on terrestrial ecosystems Development of observational techniques to monitor and assess biogeochemical feedbacks in the Earth system Theory and model development
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requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can