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Field
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slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and demonstrated experience in computer vision or analysis of pathology images
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-Language Models (VLMs). The postdoctoral researcher will explore how advanced generative and multimodal models can be designed, adapted, and applied to challenging vision tasks such as image understanding
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of food ingredients. The research is focused on structural and physical aspects of food ingredients and food matrices using high resolution microscopy and image analysis. We are seeking a highly motivated
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demonstrated experience in computer vision or analysis of pathology images. The appointees will participate in a multidisciplinary collaborative research project related to development of deep learning model
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Intervention invites applications for a two‑year postdoctoral fellowship within the project AI‑Driven Medical Imaging. The position is expected to start on 1 September 2026 or as agreed. The fellowship is funded
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discipline Strong experience in integrating several of the following components: Deep learning and LLMs for molecular biology Vision foundation models for pathological image analysis Multi-omics datasets (e.g
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. ViCOROB is a research institute at the University of Girona focused on computer vision and robotics. It combines fundamental and applied research in areas such as underwater robotics, medical image analysis
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advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities such as CT, MRI, X-ray, and ultrasound. Research areas include image segmentation, detection
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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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work at the interface between computational spatial omics, single-cell mass spectrometry (MS)-based proteomics, multiplex imaging, and bioinformatics. We have recently co-developed Deep Visual Proteomics