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Field
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Project Overview We are hiring highly motivated and talented Postdoctoral Associates who are interested in advancing the state of the art in resource-efficient machine learning at the Singapore-MIT
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with causal inference methods or machine learning approaches Demonstrated experience in scientific writing and publication Ideal for candidates who: Have recently completed (or are near completion of) a
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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the HNSCC team, including Taran Gujral (machine learning-enabled drug screening), Slobodan Beronja (mouse models of HNSCC), and Patrick Paddison (functional genomics). This work will encompass a broad array
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, electrical and computer engineering, data science, informatics, biomedical engineering, or a related field. Preferred: Demonstrated expertise in AI-driven drug discovery, machine and deep learning
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to formulate mathematical models of the problems and develop efficient solution methods, particularly by leveraging techniques from machine learning and operations research. b) The applicants are expected
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, measure transport and machine learning on developing a novel mathematical framework for identifying reduced dynamical models of high-dimensional complex multi-scale systems. The project will develop fast
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regression, Cox proportional regression model, Poisson regression model) and Big Data analytics (e.g., machine learning, artificial intelligence [AI], text mining, generative AI) Knowledge/Skills/Abilities
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contribute to developing this theoretical framework, with a strong focus on analytical modeling, computational methods, and the interpretation of learning signals embedded in physical structures. Recent
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Are you passionate about using data science and machine learning to address mental health inequalities in rural and coastal communities? The University of Lincoln is seeking an ambitious