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the preparation and defence of a PhD thesis focusing on the analysis of extreme events using deep learning methods. Your responsibilities will include developing deep learning-based methods for modeling extreme
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evaluation of FAPI-based radioligands, focusing on their imaging and therapeutic potential. Key tasks include: Designing, planning, and executing preclinical experiments in cell and animal models of cancer
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constraints of contemporary family complexity. The innovative integration of classical (longitudinal) methodology with digital humanities tools and methods (especially large language models) at the intersection
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, focusing on their imaging and therapeutic potential. Key tasks include: Designing, planning, and executing preclinical experiments in cell and animal models of cancer. Conducting biodistribution, dosimetry
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-hydrated alkali-silicate melts that enable efficient zeolite formation under mild conditions. Molecular modeling and experimental characterization show that the type and concentration of the alkali cation
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representations of acoustic scene information can be leveraged to model and interpret complex environments. By combining these representations with predictive processing frameworks, the project aims to enable
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from literature and of defining research objectives. You have passion for science, self-motivation, flexibility and team spirit. Experience with techniques such as cell culture, CAM model, qRT-PCR
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-motivation, flexibility and team spirit. Experience with techniques such as cell culture, CAM model, qRT-PCR, fluorescence microscopy and/or bioinformatics would be a plus. Your teaching competences are in
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the crossroads between the fields of robotics, signal processing and machine learning The candidate will explore how graph-based representations of acoustic scene information can be leveraged to model
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the European Research Council. You will develop and apply NLP methods (application and development of neural network–based models for tasks such as relation extraction, entity identification, classification, and