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Future. Discover. Together. The Applied Machine Learning (AML) group within the Artificial Intelligence Department is seeking a scientist in the field of Machine Learning to strengthen its research
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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expected to contribute to the development and application of advanced manufacturing simulations, and machine learning (ML) models relevant to additive manufacturing, virtual manufacturing, material
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therapeutic monitoring. The program addresses critical regulatory challenges posed by AI devices that can continuously learn and adapt, including the unique nature of clinical medical data with low disease
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) for engineered microbial strains. Design and implement machine learning algorithms to predict strain performance and optimise metabolic pathways. Integrate multi-omics datasets into computational workflows
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candidate will: Develop genome-scale metabolic models (GEMs) for engineered microbial strains. Design and implement machine learning algorithms to predict strain performance and optimise metabolic pathways
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quality modelling, with focus on Knowledge-Guided Machine Learning. The position is a rewarding opportunity to be integrated in an excellent freshwater group. The department’s research and advisory
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
quality modelling, with focus on Knowledge-Guided Machine Learning. The position is a rewarding opportunity to be integrated in an excellent freshwater group. The department’s research and advisory
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Future. Discover. Together. The Applied Machine Learning (AML) group within the Artificial Intelligence Department is seeking a scientist in the field of Machine Learning to strengthen its research
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced