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modelling is a bonus. Good verbal and written communication skills, especially in Danish, will also be valued. International applicant? Aarhus University offers a broad variety of services for international
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drought early warning and monitoring system for large-scale river basins. The project will explore both data-driven and model-based approaches for drought predictions, paving the way for a continental high
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on designing and evaluating cognitive support interventions, working with stakeholders to co-design techniques and assess their impact through controlled studies. This suits candidates with strong user-centred
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control, or auditing. The Department of Accounting is one of the oldest and largest accounting departments in Europe. We have a growing and flourishing research environment, covering core research areas
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firn- and hydrology modelling. Your work will contribute observations that are essential for evaluating firn- and hydrological models and for understanding ice-sheet-wide change. Your results will be
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and corrective feedback. You will apply advanced algorithms for machine learning, multimodal biosignal processing, and human-state inference, working with shared-control strategies and electrotactile
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PhD position in environmental toxicology and endocrine disruption: Focus on new endpoints in zebr...
systems that reveal possible endocrine disrupting effects of chemicals in model organisms, for example, fish and invertebrates. Research project: The objective of the project is to investigate and pre
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on the solid application of scientific research method. The department has a pluralistic approach to research methods that includes, for example, formal theoretical models, quantitative empirical methods such as
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deviation from the healthy distribution. But in the absence of labels, how should we direct the model to learn relevant features, and how can we determine which features are relevant? These questions
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and structure of healthy anatomy and detect any deviation from the healthy distribution. But in the absence of labels, how should we direct the model to learn relevant features, and how can we determine