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for multimodal machine learning, combining large-scale image data with molecular profiling and clinical data. This includes, for instance, research on deep learning-based image analysis and data assimilation
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stereolithographic, 3D printing and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro
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stereolithographic, 3D printing and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro
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and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR
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dynamics and heat transfer research. Programming skills in Python and MATLAB, particularly in machine learning, data analysis, and image processing. Experience working in Linux environments. Ability
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a focus on visual language grounding, in other words, the linking of elements of natural language (words, phrases, or sentences) to visual inputs (such as images or video) in a meaningful way. The
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to Reionization from observational and numerical perspectives. Main responsibilities The observational projects will involve analysis of both imaging and spectroscopic data obtained from JWST, HST, and other
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grounding, in other words, the linking of elements of natural language (words, phrases, or sentences) to visual inputs (such as images or video) in a meaningful way. The position is part of an on-going
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are: DWI-MRE: Adapt current MRE sequences to estimate both MRE and diffusion-weighted images (DWI) at once. MD-MRE: Adapt DWI-MRE to use free gradient waveforms as done in MD-dMRI. Process MD-MRE using
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. Project description: DDLS Fellows Program Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular