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-learning algorithms Versatile data-science knowledge, including image and DNA sequences processing Programming skills in Python or other modern programming languages supporting AI and bioinformatics
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: PhD in biology, physiology, biomedical engineering, or a related field. Demonstrated skill in handling mouse and rat pups, small animal procedures, and in vivo imaging. Familiarity with cardiac
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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored
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combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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work aimed at combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you
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: Application letter/cover letter Curriculum vitae (with applicant’s e-mail address & telephone number) Documentation of qualifications (Master and PhD diploma, etc.). Complete and numbered list of publications
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. The file must include: Application (cover letter) CV Academic Diplomas (MSc/PhD – in English) List of publications Links to relevant public code repositories that you may have contributed (optional) 2
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); units such as onboard computers, mass memories, remote terminals and instrument control units*; digital and analogue signal processing electronics for payload/platform functions; front-end acquisition and
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data and clinical information. Applicants must hold (or be close to completing) a PhD in a relevant field and have expertise in modern computer vision and AI research. Experience with biomedical data