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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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biomedical data types Machine-learning and statistical modelling Mechanistic models of biological systems Python, R or equivalent programming environment Documented knowledge and experience in cancer biology
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is developing research excellence in areas including Machine Learning, Big Data, Visual Analytics, Computational Intelligence, Machine Learning, Software Implementation and Testing, and their
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, statistical inference and machine learning, with potential applications to practical problems Independent teaching Supporting research work, participation in research projects, and assistance with teaching and
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of the central challenges on the path toward large-scale quantum computing. In this PhD project, you will investigate how machine learning can enable faster, more scalable QEC decoding. The goal is to develop new
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these data sets and machine learning, which can be combined with real-time satellite-based AIS data and sensors such as radar and algorithms for anomaly detection, risk analysis and autonomous navigation
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frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems
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machine learning methods to scientific data (e.g. multivariate analysis, chemometrics, neural networks, classification or regression models); familiarity with large language models (LLMs) and other
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strong programming, experimental-design, and quantitative-analysis skills Have experience working with large-scale data, machine-learning models, human-subject studies, platform audits, or computational
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).The PhD candidate is expecting to assisting in teaching and help in laboratory on the related topic. Applicants should fulfil the following requirements: A Master's degree in computer engineering, computer