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. · Experience in clinical research or clinical trials is considered an asset. · Computer experience required and proficiency in Microsoft office, GraphPad prism, flow Jo, Photoshop, image J and NIS-Elements
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! Submitting a cover letter, CV/Resume to assist us with the review process. You may upload these documents on the application under CV/Resume. Required Education and Experience Appropriate PhD in a related
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machine learning, computer vision, and medical image analysis, with publications in top-tier AI and medical image analysis conferences and journals, including CVPR, ICCV, ECCV, NeurIPS, MICCAI, TPAMI, TIP
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. Processes and analyzes patient-derived tumor and blood samples in collaboration with clinical teams, following IRB and biosafety protocols. Performs molecular and cellular assays, including RNA and protein
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models, for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics), large textual corpora (e.g., scientific literature), and/or pathologic images. Our research
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(Mechanical Engineering at UCL) will also collaborate, he specialises in imaging of additive manufacturing and will support the project by assisting with the in-process monitoring. We expect that the PhD
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to identify, map, and measure in airborne and satellite images of the Earth, the natural and socio-environmental hazards and risks studied in IRIMA(earthquakes, tsunamis, volcanic eruptions, landslides
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organosulfur compounds using infrared and/or ocean color observations. Processing and analyzing satellite imaging spectroscopy data and integrating these with in situ and geophysical datasets. Lead dissemination
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mathematicians, and computational scientists, and building worldwide collaborations aimed at tackling the challenges posed by terabyte and petabyte-scale imaging data processing, visualization, and dissemination
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with cryo-EM/ET data processing, tomographic reconstruction, or related inverse problems in imaging. Familiarity with molecular dynamics simulations (e.g., OpenMM, LAMMPS) and/or synthetic data