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Field
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: Letter of motivation and academic CV Organisational Unit Inverse problems and mathematical image processing is one of the research areas at the Department of Mathematics and Scientific Computing, which is
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Qualifications PhD in cell biology and biophysics. Training and/or experience in handling of purified proteins, computational analysis and modeling in MATLAB, stem cell differentiation and tissue culture, confocal
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 7 hours ago
, Computational Biology, Information Science or a related field. Required Qualifications, Competencies, and Experience Experience in at least one of the following fields: Large Language Models, Image Processing
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Computer Science or a related field, with a focus in databases, data systems, theory, or algorithms. Strong publication record in top-tier venues. Solid background in one or more of the following: Query processing
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computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate hydraulic flood
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Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate who will support the areas of multimodal imaging, AI architecture, and efficient
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for experiments Job Summary The Park research group has an opening for a postdoctoral research fellow within the College of Pharmacy, Department of Pharmaceutical Sciences. We are seeking highly motivated
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surgery. Ideal candidates will have: A Master's degree in artificial intelligence, bioinformatics, computer science, physics, mathematics, or a similar field. Proficiency in deep learning and exceptional
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character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science
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demonstrate the uni-directionality of their motion. This research is funded by the Multilateral Academic Projects (MAPS) program of the Swiss National Science Foundation, which supports interdisciplinary