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on machine learning solutions and data visualisation. In addition will some cod individuals be tagged, and their behaviour be monitored using acoustic telemetry. The cod behaviour could also be correlated with
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bioinformatics methods have made significant strides, AI approaches - particularly deep learning - are revealing patterns and relationships in biological data that were previously inaccessible. As a postdoctoral
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great advantage: Forest and wood production processes Wood construction Furniture manufacturing Wood material science Machine learning Process simulation and optimisation The postdoctoral fellow is part
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proposal that will be further developed and contextualized in collaboration with the research leader in the research environment. Your research topic needs to be relevant for student learning in higher
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-assembly mechanisms, identifying robust experimental signatures of collective properties, exploring practical applications, and utilizing artificial intelligence and machine learning to aid in this process
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high level of motivation and commitment, good aptitude/skills towards laboratory work and the willingness to learn. The applicant must also be organized, rigorous and be able to work both independently
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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in labour unions, etc. We seek a talented and open-minded candidate, who is eager to learn and has a genuine scientific interest. Extensive knowledge in and practical experience with protein expression
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to solving applied problems through research, collaboration and education on sustainable plant production. We teach and research plants for food, feed and energy. The research and teaching focus