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imaging, multi-modal clinical and omics data, and explainable AI (XAI) for the prediction of hepatocellular carcinoma (HCC). Your research will directly contribute to early detection and risk stratification
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of images (like in comics) in relation to the structure of languages. Additional information about this research project can be found at www.visuallanguagelab.com/pictree . Your position The PICTREE Project
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& Computer Science of the Eindhoven University of Technology in the field of “Geometric Learning for Image Analysis”.The two year postdoc position is part of VICI Project (VI.C. 202-031, PI: R.Duits) and will
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affects the performance of plasmonic structures in photovoltaics and imaging. About this position In this position you will leverage materials with high thermal conductivities (e.g. 2D materials) to achieve
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will image them using a variety of microscopy methods, and collaborate with a team of computer vision scientists to build ML-based models for phenotype prediction, helping to accelerate the cell
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well as working with organ-on-chips and imaging of these devices will be considered as a benefit; Excellent analytical skills and an innate ability for solution oriented problem solving; Team player with great
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now includes five years of follow-up data. You will focus on linking the molecular data with clinical data, which is being analyzed by clinical researchers. Additionally, you may integrate imaging data
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applications involving image analysis, real-time monitoring, or complex process optimization. You have hands-on experience with model optimization techniques such as post-training quantization (PTQ
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-oriented mindset, and a passion for (scientific) challenges, you are the right person for us. Our lab develops technologies that integrate advanced imaging, computational analysis, and single-cell & spatial
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(GenIR), a new and rapidly evolving retrieval paradigm where generative models are used to directly generate document identifiers given a user query. This paradigm departs from traditional multi-stage