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DTU Tenure Track Assistant Professor in Nutrient-Focused Processing in Ultra-Processed Food Syste...
, bioactive compounds, and other key nutrients. Develop and apply machine learning and modeling techniques to analyse, predict, and optimize the effects of processing on food composition, food Ingredient
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. Biomedical data science that combines methodology and implementation, in areas such as statistical modeling, natural language processing, bioimaging analytics, and machine learning/artificial intelligence
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candidate will be appointed to a full-time tenured position at the rank of Associate or Full Professor within the Faculty of Engineering. In addition to leading a world-class research program, they will teach
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through soft, disordered materials, including auto-regulated networks, composite soft solids, and exotic photonic biomaterials. The lab has two fully funded PhD and/or postdoctoral positions available
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of statistics, bioinformatics, and/or machine learning approaches are desirable but not required. This is a permanent position within the Nature Portfolio. The successful applicant will primarily support Nature
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onboarding period that includes specialized courses and hands-on training in AI and machine learning. You'll also have the chance to explore different labs and core facilities, meet fellow researchers, and
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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multi-omics data integration and the project will provide opportunities to learn, develop, and apply machine learning and deep learning methods on genomics data. Requirements: excellent university and PhD
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Engineering, etc.), expertise in cutting-edge AI and machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role
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. Candidates must have: A PhD with a strong component in machine learning and deep learning (or a PhD defense planned for 2025), Research contribution in deep learning and application domains such as computer