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Field
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. • The ability to work independently and collaboratively within a multidisciplinary team. • Strong writing, critical thinking, communication, and presentation skills. • Experience in Machine Learning is a
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PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning for lung cancer imaging data; - identify and select the appropriate methods for the study in question; - develop
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following mandatory requirements: a) A completed degree in Computer Engineering; b) Good knowledge in the areas of Machine Learning, Natural Language Models, and Computer Security – information to be provided
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, machine learning, and AI techniques to analyze complex datasets and uncover actionable insights. Co-author and support high-impact, interdisciplinary research publications in leading sustainability and
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, together with significant experience in computer programming and computational biological applications. A strong background in statistics and biology. Formal training and/or research experience in genomics
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or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented
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Responsibilities: To perform pioneer research in scent digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different
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algorithm development, modeling machine learning, and scientific simulation ▪ Ability to work well in an interdisciplinary environment, and to collaborate with experimentalists ▪ Strong oral and written
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into operational use cases. Prepare data collection frameworks and work on fish health monitoring datasets for machine learning training and benchmarking. Support the development of translational “lab-on-farm
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics