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Field
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of response and develop predictive models. The work will involve analysis of large-scale datasets through multiomics integration, machine learning, statistical genetics, QTL analysis and development of genetic
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, computational biology, statistics or a closely related field. You have strong programming skills, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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candidate with a strong interest in organ transplantation and ex situ machine perfusion, regenerative medicine, and translational biomedical research. You enjoy experimental work, can work independently, and
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biology • Multi-omics data integration and analysis • Pharmacogenomics and computational drug discovery • Pharmacogenomics and precision medicine • AI and machine learning applications in biomedical
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high-resolution mass spectrometry, in vitro pharmacological characterisation of new psychoactive substances, as well as metabolomics and machine learning. As a PhD student, you devote most of your time
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and machine learning methodology to help deal with key challenges in developing such models in large-scale observational electronic healthcare record data. These models will be applied to important real
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researchers in the Faculty of Medicine and at Aalborg University Hospital. Applicants should have: A strong technical background in machine learning, computing, data science, biomedical engineering, or a
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, computer vision or machine learning or Documented Experience with 2D or 3D biomedical imaging, quantitative or multimodal biological datasets. Familiarity with biomaterials, tissue engineering, scaffolds
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Is the Job related to staff position within a Research Infrastructure? No Offer Description We invite applications for a three-year PhD Research Fellowship in probabilistic machine learning and