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statistical, machine learning, and artificial intelligence (AI) techniques to analyse 'omics and clinical data, and contributing to the development of biomarkers and predictive models. A critical part of your
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team. Preferential factors: academic performance, with a focus on Machine Learning and Biomedical sciences previous experience (e.g., research, professional, lecturing) in the domains of the grant
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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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their research projects, and advising on methodology and academic writing Required selection criteria You must have completed a doctoral degree in (machine learning, statistics, or similar). You must have a
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large scale flow network simulations, machine learning, and methods from topological data analysis, to a broad set of problems. Examples include modeling vertebrate and invertebrate circulatory systems
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* Experience with image analysis or medical imaging Established record of peer-reviewed scientific publication Prior experience / comfort with computer programming is a plus Modes of Work Positions that
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have experience with applications of machine learning and deep learning on medical image data that you have experience applying methods within generative artificial intelligence to medical images and
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to build up a researcher profile and competence that qualifies the person to apply for a position as associate professor. The postdoctoral researcher will focus on the work package 'Learning, economic policy
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desirable with a willingness to learn new skills. The post holder will be required to work independently and as part of a team and be computer literate with excellent communication skills. This is an
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datasets (e.g., images, surveys, statistical and sensor data). Familiarity with geostatistical, GDAL, Python, PostGIS/PostgresSQL, Machine Learning, AI, Internet of Things and Remote Sensing datasets is