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, evaluating, and fine-tuning machine learning models (e.g. deep neural networks) to segment underwater scenes and classify anomalies. The work will explore the use of virtual environments and synthetic datasets
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale
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fantastic opportunity for ambitious computer scientists to join our Computer Science Graduate Teaching Assistant (GTA) Programme! How does it work? Candidates will study for a four year, full time funded PhD
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Bård Halvorsen 1st September 2025 Languages English English English Digitalisation and Society PhD position - My AI Co-worker Apply for this job See advertisement About the position We are seeking a
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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This
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/ . The post offers an exciting opportunity for conducting internationally leading research on the whole spectrum of novel machine learning algorithms and practical medical imaging applications, aiming
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machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
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machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
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performance. What you can expect Modelling. Apply probability theory, statistical analysis, and machine learning techniques to analyze and interpret market behavior Alpha Monetization. Blend quantitative
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://romeijn.web.rug.nl/paper/2023_romeijn_-_VICI_project_description.pdf ), where you can find a detailed list of planned PhD and postdoc positions. Candidates can freely choose their research topic within the domain