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for application to fusion reactors, considering factors such as muon flux, detector sensitivity, and data analysis algorithms. PhD candidates will be tasked with developing simulations of complete detectors and
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will be able to teach one of the undergraduate courses related to AI/ML, programming languages, data structures and algorithms, operating systems, network security, visualization, and human-computer
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data and machine learning processing pipelines. It will also focus on developing AI algorithms for semantically enriching data assets with domain knowledge, which feed into building knowledge graphs
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ecosystems. Fairness, safety, interpretability and trust in AI algorithms and AI-based technologies. Applications with healthcare, energy, sustainability, business analytics, etc. Candidates are expected
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Learning or Machine Learning algorithms Experience applying quality system standards, software development standards and regulation, e.g. ISO 13485, IEC 62304, MDD, and MDR Experience working within a team
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leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis of normative frameworks and aggregation rules, and
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range of computing subjects, including Cyber Security, Programming, Algorithms, Computer Logic and Architecture, Software Engineering, Database Design, both at undergraduate and postgraduate level
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with the regulatory environment around Deep Learning or Machine Learning algorithms Experience applying quality system standards, software development standards and regulation, e.g. ISO 13485, IEC 62304
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Algorithms Agile Programming Applied Machine Learning Computer Graphics Web Design and Programming Cloud Technologies Advanced Databases and Big Data Introduction to AI Natural Language Processing DevOps Data
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, applying and validating your algorithms through laboratory experiments. A PhD in Chemistry, Biology, Computer Science, or related fields with a focus on protein engineering. Proficiency in Python programming