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, clinical trial of behavioral treatment for children with obesity and their families. This position provides an outstanding opportunity to build unique and deep experience and expertise in designing
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variational models and deep learning techniques. You will implement and validate reconstruction algorithms, ensuring their performance, robustness, and efficiency for clinical application. You will participate
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genomic data for reconstructing evolutionary patterns and processes that have shaped biological history across deep timescales. The ideal candidate will have a background in phylogenomics and bioinformatics
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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
should possess a doctoral degree in Educational Measurement, Psychometrics, Quantitative Psychology, Statistics, or a related field. They should have a deep familiarity with modern test theories, and a
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of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In
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or equivalent Research FieldEngineering » OtherEducation LevelPhD or equivalent Skills/Qualifications Skills in acoustics (PhD in acoustics required) and acoustics software. Skills in machine learning and deep
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, analysis and deep learning, 2) biostatistics and health/clinical data analysis or 3) cellular and molecular techniques will be an advantage. Opportunities for publication and independent development will be
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 7 days ago
of Epigenetics and Stem Cells studies fundamental epigenetic mechanisms shaping development, evolution, and disease. We employ cutting-edge technologies — from single-cell multi-omics and deep learning to live
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knowledge of methodologies such as deep and statistical learning. Informal enquiries may be addressed to Prof. Andrea Vedaldi (email:andrea.vedaldi@eng.ox.ac.uk) For more information about working at
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis