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in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages, PyTorch Familiar with foundation models (vision large models or multi
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the Phytophotonics department, you will work as part of an interdisciplinary team at the interface between plant sciences and optical technologies. The focus is on various spectroscopic and imaging methods
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(or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject area: Medical imaging, biomedical engineering, computer science & IT
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) in an appropriate discipline. Subject Area Computer Science & IT, Electrical & Electronic Keywords Artificial intelligence, floating-point arithmetic, numerical analysis, computer arithmetic, machine
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background in biology, programming or mathematics is meritorious. Knowledge in medical image processing, image registration, and large-scale analyses of genetic (including Mendelian randomization), protein, or
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(bio-)chemistry, physics, and engineering expertise to study molecules and cells, taking advantage of optical and single-molecule imaging, molecular probes, molecular biology, and 'large' data analysis
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molecular biology, quantitative imaging and biophysical approaches to investigate cell shape changes in cultured cells and in vivo. Current projects in the lab include investigating the regulation
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-performance scientific imaging systems, based on the latest sensor technology, for the next generation of astronomical imagers, spectrographs and adaptive optics systems, as well as possibly equivalent detector
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Phenomena , Cold Atom Physics , Cold Atoms and Molecules , Cold QCD Group , collider physics , Collinear Fast Beam Laser Spectroscopy , Complex Quantum Systems , Computational Materials Sciences
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manufacture, to enable quantitative imaging. Your research will include a mix of computational and experimental work to develop and characterise these instruments. Monte Carlo simulations (using GEANT4) will