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experience with natural language processing and/or machine learning (e.g., through first/co-authored publications) Demonstrated interest in interdisciplinary research at the intersection of AI and law
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data integration and analysis Integrate phylogenomic and functional data using machine-learning approaches for candidate gene prioritisation Contribute to software and web-tool development Present
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-Physical Energy Systems The PhD position focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be
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within this field. Your work tasks In this position you will conduct research within Computer Vision and Deep Learning, with a particular focus on the development of an AI-powered framework
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by the Carlsberg Foundation. SMARTbiomed is a research center with core mission to develop statistical and computational methods focusing on causal inference, risk prediction and machine learning
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machines Conduct simulations and experimental testing to validate system performance Document and disseminate research results through scientific publications and presentations The PhD candidate will work
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and qualifications A PhD degree in computational biology, machine learning, computer science, data science, bioinformatics, or a related discipline Demonstrated machine learning experience in form
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such as Google Earth Engine statistical modelling, machine learning, cloud/high-performance computing retrieving ecologically relevant environmental data from national to global databases research and/or
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supervision of Master’s students and PhD researchers. Take part in open science and code sharing. Skills, expertise and qualifications A PhD degree in computational biology, machine learning, computer science
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theoretical challenges motivated by astrodynamics, optimisation, control, scientific machine learning, mission design and autonomous systems, translating these into new research directions in deep learning