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on developing the imaging system as well as novel machine learning approaches for image analysis and disease classification using field data from German and Brazilian agricultural trials. Responsibilities Design
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for reinforcement for our team (Computational Imaging Research Group) that creates novel imaging systems with unprecedented capabilities. Be part of change Literature review on state-of-the-art machine learning
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programming languages such as Python and experience with deep learning frameworks (e.g., PyTorch, TensorFlow) is highly desirable strong interest in interdisciplinary research combining imaging, machine
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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Future. Discover. Together. The Computer Vision & Graphics group of the Vision & Imaging Technologies (VIT) department is looking for a student assistant in the area of deep learning for scene
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analysis in cleared tissues Working knowledge of image analysis and automation is welcome Examples: Fiji ImageJ, Python for batch processing, napari, basic ML or deep learning toolchains Advantageous but not
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medical datasets, particularly images and texts, as well as tabular and higher-dimensional data Testing, implementation, and benchmarking of new research methods Development of machine learning approaches
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protocols to characterize both cellular and vascular properties of the TME. The approach will be validated using a combination of in silico models, computer simulations, and in vitro experiments using tumor
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tasks and seminars, training of new group members in laboratory techniques) Independent work ethic and desire to learn new methods and protocols Basic R computer programming skills Basic software skills
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degree in computer science (or a related field) Rich experience in devising machine learning models, methods, and algorithms for computer vision and image processing. Scientific track record with