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according to their background and interests. Applicants should have strong quantitative and computational skills (e.g., text or image analysis, natural language processing, large-scale data methods) and a
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scientists, clinicians, and engineers to push the boundaries of brain imaging and contribute to insights that can improve healthcare practice. Technical PhD Candidate In this role, you will be responsible
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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data
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to provide the first comprehensive picture of Hsp90's structure–dynamics–function relationship, with broad implications for understanding cellular health and disease. As a PhD student on this project, you will
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the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography
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achieving lower system costs. More information on the project: https://radarmimo.com/4d-imaging-automotive-mimo-radar/ The selected PhD candidate will work on one of the following research topic: Optimal
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PhD students, you will further perform human testing of several prototype devices developed at Delft University of Technology and you will cleverly review and interpret the measurement data
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representations. In this project, you will substantially improve quantitative magnetic resonance imaging (MRI) image quality using deep learning approaches. Quantitative MRI allows healthcare providers
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process (process-specific design) (2) Optimization of the LPBF process parameters to achieve desired microstructure and material properties, (3) In-depth materials, and property testing and (4) Prototype
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- Experience in image processing and/or - Familiar with rodent behavioural tests and/or - Experience in histology and/or - Familiar with the theory and preferably practical aspect of MRI. You will be responsible