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statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability
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, you will work at the intersection of polymer processing, materials science and Machine Learning to develop dynamic recipes for sustainable plastics. In a typical plastics production line, several types
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organizational theory, the learning sciences, digital transformation, digital technologies, human-computer interaction, and related fields. Within the specific field, the PhD student will engage in both research
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computational methods as the necessary foundation to venturing and expanding the field through modern approaches in machine learning and artificial intelligence. The appointment is hosted by the Department
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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for Statistical and computational Methods for Advanced Research to Transform biomedicine (SMARTbiomed) within the field of statistical and machine learning methods development for genetic analysis and causal
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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technologies Development and utilization of high throughput methods for characterizing and quantifying the physicochemical behavior of food macromolecules in complex matrices. Modelling and the use of machine
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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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: Conceptualisation and synthesis of data integration and visualisation workflows. Data management and the development of knowledge graphs Development and application of AI and machine learning methods and pipelines