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Field
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developed mouse models and human tissue processing pipelines. The candidate will learn and use cutting edge immunological methods (high parameter flow cytometry, multiplex imaging, single cell and spatial
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related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis
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of breast cancer patients (the largest of their kind; making extensive use of imaging mass cytometry and spatial transcriptomics) that span observational studies and clinical trials. We must precisely define
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an image. Modern inverse freeform design methods compute surfaces that convert a given source light distribution to a desired target light distribution. These can be used to guide the design process for
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for the position. Preferred selection criteria Experience with machine learning and neural networks Basic knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux
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knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux systems and High Performance Computing Good oral and written presentation skills in Norwegian
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of Amsterdam, under the supervision of Prof. Noushine Shahidzadeh (expert on crystallization and sol-gel process in confinement) and Prof. Hannelore Derluyn (expert in micro-CT 3D imaging for porous media
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fracture mechanisms in heterogeneous or composite materials. Development and implementation of advanced constitutive models and computational mechanics methodologies. Processing and analysis of experimental
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deep learning, computer vision, medical image analysis or unsupervised learning is an advantage. English language skills, both written and spoken Qualification requirements PhD stipends are allocated
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newly born neurons, combining developmental neuroscience with structural biology imaging. This research will uncover fundamental mechanisms of brain development and provide insight into processes