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have: A PhD (or equivalent) in a relevant discipline (e.g., biostatistics, machine learning, computer science, clinical informatics, natural language processing). Strong skills in data analysis and
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of nondiscrimination is located at uab.edu/titleix . Department: Electrical and Computer Engineering Assignment Category: Full-Time Tenure Status: Non-Tenure Track Required Qualifications: Applicants must hold a PhD
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at: www.fz-juelich.de/gp/Careers_Docs Further information on doctoral degrees at Forschungszentrum Jülich (including its various branch offices) is available at https://www.fz-juelich.de/en/careers/phd We
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for biomedical/healthcare and green energy applications, big data and AI in smart sensor technology, and quantitative and systems biology. These efforts are supported by infrastructural and internationalisation
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/or spatial genomics, computational biology, machine learning, bioinformatics, and systems neuroscience. Prior experience with deep learning applied to biological data is a plus. Practical experience
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data in any area of finance, such as asset pricing, machine learning, ESG investing, how social networks affect finance, research replicability, regulatory data in finance, financial institutions, and
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. The research in the PhD project will focus on core spatio-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation
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, neuroimaging and clinical psychiatry, with direct clinical impact. Your main activities are: analyzing and integrating multimodal MRI data for biotype identification; applying machine learning and advanced
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application! We are looking for a PhD student in Statistics with placement at the Division of Statistics and Machine Learning, Department of Computer and Information Science. Your work assignments As a PhD
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) within the Department of Computer and Information Science . At STIMA we conduct research and education in both statistics and machine learning, at the undergraduate, advanced and PhD levels. We regularly