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Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position
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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
We welcome applications from candidates with a broad range of academic backgrounds and experiences for a 4-year PhD project on Mathematical and Machine Learning Aspects in Crystallography at Durham
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that truly understands its environment. You have a master's degree in Computer Science, Artificial Intelligence or similar. You are interested in Logic, Machine Learning, Knowledge Graphs, Stream
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) to improve cardiovascular risk assessment and prediction. Research Aim This PhD research aims to develop and evaluate AI and Machine Learning approaches for early prediction, risk stratification and prognostic
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for Artificial Intelligence (AI) and Machine Learning (ML) to support earlier and more personalised healthcare. Research Aim This PhD research aims to develop advanced AI and Machine Learning methods
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learning, or human-computer interaction would be advantageous. How to apply We are seeking expressions of interest from qualified domestic candidates who wish to apply for this PhD opportunity. This position
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• Possess a PhD in a related discipline (transportation engineering, operations research, computer engineering/science, or related disciplines) by December 2024. • Strong knowledge and
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technologies aimed at assessing, protecting, and repairing donor livers before transplantation. This PhD project is embedded within the multidisciplinary CRISPR-CRYSTAL consortium, bringing together experts in
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of response and develop predictive models. The work will involve analysis of large-scale datasets through multiomics integration, machine learning, statistical genetics, QTL analysis and development of genetic
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-based, and machine-learning approaches - Identifying markers of intelligent, intentional, and malicious manipulations of detection systems - Developing detection and classification methods to distinguish