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prostate cancer risk across diverse ethnic groups. This work aims to support more equitable risk stratification in cancer screening programmes. Using simulations based on multistate modelling framework
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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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Europe | 22 days ago
industry.Key Features of the PhD PositionsMultidisciplinary training: Training of DCs will exploit multidisciplinary consortium expertise spanning design, modelling and simulation of photonic systems, sensor
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models. This theoretical project will facilitate close collaboration with experimental groups and enable benchmarking of theoretical predictions. The PhD researcher will be part of the Correlated Quantum
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for funded Graduate Research Assistant (2-Master and 1-PhD positions) in the Flow Assurance Lab at Universiti Teknologi PETRONAS.The successful candidate will work on research related to multiphase flow
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communication environments. For the advertised PhD researcher position, this work involves written and multi-modal corpora development and analysis, and working with computational models of irony and analysis
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/emissions modelling preferred but not required Creative problem-solving skills and ability to work independently *Candidates with a PhD in other disciplines may be eligible if they can demonstrate exceptional
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for clinical use. Generative and Predictive AI for Clinical Decision Support and Statistical Inference Develop biologically informed statistical methods and uncertainty estimation models to train deep learning
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Description Who We Are: The Cutsail research group at Ludwig-Maximilians-Universität (LMU) Munich is seeking a highly motivated PhD student / research assistant for full-time, on-site work in the
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to produce resilient and high-performing models. · PhD in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field Strong track record of applying ML in academic or industry