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materials science, and machine-learning-driven materials discovery. The successful candidate is expected to have experience in areas such as high-throughput DFT calculations, thermodynamic and phase
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: Overcoming Inequity in Embodied Learning in Danish Vocational Education and Beyond , funded by Independent Research Fund Denmark. This is a full-time (37 hours per week), fixed-term (24 months
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teaching assistantship duties. Expected Skills and Qualifications: We are seeking a candidate with a strong desire to make significant contributions to fundamental machine learning research
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intersection of polymer processing, materials science and Machine Learning to develop dynamic recipes for sustainable plastics. In a typical plastics production line, several types of materials are fed into
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which you will be automatically redirected to an external site after 5 seconds) Save job Click to add the job to your shortlist Send job Job Details Are you interested in using machine learning to
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computational pipelines for QTL data integration and analysis Integrate phylogenomic and functional data using machine-learning approaches for candidate gene prioritisation Contribute to software and web
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reactor. The work combines physics-based thermal design and process-level system simulation with high-fidelity computational fluid dynamics and fast reduced-order and machine-learning models, so that the
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the development of knowledge graphs Development and application of AI and machine learning methods and pipelines. Qualifications Ideally, you hold a PhD in Data Sciences and have several years of
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data visualization, with a track record of designing and building visualization dashboards. Research experience in human-centered AI, or in the integration of AI and machine learning methods into
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Biotech Research and Innovation Centre, The Lund Group, University of Copenhagen | Denmark | about 1 month ago
project is conducted in collaboration with the deep probabilistic programming group of Thomas Hamelryck: https://di.ku.dk/english/research/groups/machine-learning-in-biology/?pure=en/persons/271052