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collaboration with the Department of Computer Science at Aalborg University, combining socio-technical research on human-robot collaboration with technical research on interaction technologies and robotic systems
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group of international scholars working with fieldwork methods, design interventions, and digital/computational methods to engage users, publics, and partners in exploring dilemmas, controversies, and
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Prediction (AI-focused) This position focuses on developing cutting-edge AI methods for genetic risk prediction across multiple cancer types, with a strong focus on model performance and explainability
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and effective program delivery. Competencies The position requires strong conceptual and methodological competencies, integrating both quantitative and qualitative approaches to study transformation
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fundamental AI methods together with their software implementations for interpretable statistical fault prediction and lifetime assessment in the context of Structural Health Monitoring of operating wind
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human-computer interaction, computer science, software engineering, digitalization and application development, or a related discipline. Experience with user studies and design methods will be considered
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modern machine learning methods, particularly attention-based neural networks within computer vision and/or natural language processing, and you are comfortable working with data-driven approaches
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learning, for offshore industrial produced water treatment processes. The developed methods/solutions should be tested and demonstrated on a globally leading pilot-plant sited at Aalborg University
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vision methods for analysis of the non-verbal communication of humans in a therapy setting (client + therapist). This includes working with human pose estimation, facial expressions and micro-expressions
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computational and experimental approaches, leveraging the large-scale Microflora Danica dataset combined with samples taken from sites undergoing restoration. You will help conduct sampling, refine and apply