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modelling methods to design resistance-proof antibiotics. You will join an interdisciplinary team, integrating machine learning, medicinal chemistry and microbiology. You will work with Asst. Prof. Eli N
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data
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modelling on the other. Composite and nanostructured materials are in focus due to their superior functional and structural properties. Qualification requirements Appointment as Postdoc presupposes scientific
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-theoretical modelling on the other. Composite and nanostructured materials are in focus due to their superior functional and structural properties. The position is announced as part of the DFF1 project
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. Sustainability is central to ourwork in environmentalassessment and marine management. In surveying and land use, sustainability is reflected in ourefforts in nature restoration and climate adaptation. As society
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Postdoctoral Researcher with a strong computer science background and demonstrated expertise in deep learning and generative model development to lead the AI component of this initiative. Responsibilities and
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Postdoctoral Researcher in Natural Language Processing and Digital Humanities (18 months, full-time)
research task is to model semantic change and conceptual structure using Natural Language Processing. We will build customized NLP pipelines for premodern Greek and humanistic Latin, train and evaluate word
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2026 or as soon as possible thereafter. The position seeks to strengthen and complement the Department’s ongoing activities in freshwater ecology, particularly aquatic ecosystem modelling and water
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Postdoc in Biomedical modelling of the human colon and surrounding organs Department of Computer Science Faculty of Science University of Copenhagen The Department of Computer Science invites
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computational datasets of disordered materials based on density functional theory calculations and training machine learning models to accelerate the predictions. This work will involve collaboration with Assoc