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Field
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implementing bioinformatics pipelines from raw data, applying a range of advanced statistical, machine learning, and artificial intelligence (AI) techniques to analyse 'omics and clinical data, and contributing
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learning, the topology and geometry of data, or the dynamics of learning. The successful candidate should have, or be expecting soon to receive, a PhD in Mathematics, or related field, with demonstrated
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transferring learning from other geographic regions and data types, machine learning methods, Bayesian inference and interrogation theory. The post may involve travel to Iceland and Italy in support of your work
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, machine learning, and life cycle assessment, we aim to create sustainable wearable systems to enhance human well-being. For more details, please view https://www.ntu.edu.sg/mse/research . We are looking
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Stig Brøndbo 12th October 2025 Languages English English English Faculty of Science and Technology Postdoctoral Research Fellow in Machine Learning Apply for this job See advertisement The position
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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implementing bioinformatics pipelines from raw data, applying a range of advanced statistical, machine learning, and artificial intelligence (AI) techniques to analyse 'omics and clinical data, and contributing
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Qualifications: Knowledge of kinetic modeling and/or machine learning interatomic potentials. Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully pass a pre-employment
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initiative, and may have lead responsibility for discrete components of a larger project under the general direction of a senior or principal researcher. Minimum Education and/or Work Experience A PhD is
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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental