192 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions in Sweden
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machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome, etc.) development of predictive models and digital decision-support
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to machine learning is well funded and continuously publishes in high impact journals. We foster a creative working environment, where you will find freedom to implement, develop, and publish research
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Umeå University is offering a PhD position in Computing Science with a focus on machine learning for graph
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value chains to enable AI-based applications, using methods and models from e.g. operations research, data analytics or artificial intelligence/machine learning. Identify, structure and prioritise
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at the Division of Data Science and AI at the Department of Computer Science and Engineering . Join our innovative team and contribute to exciting research in theory of machine learning, in a collaborative and
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risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include
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data using multivariate statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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Biosciences, which offers an international, collaborative, and open-minded research environment. Please visit the lab’s webpage for more information: https://erdemlab.github.io . The Erdem research group is
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-order modeling, or machine learning Experience collaborating in interdisciplinary research teams What you will do Develop hybrid quantum–classical methods to improve simulation and prediction