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Area of research: Scientific / postdoctoral posts Job description: Postdoc (f/m/x) in Deep Learning and Digital Pathology 102827 Full time 39 hrs./week Neuherberg near Munich Partial Home Office
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-Geometric Foundations of Deep Learning or Computer Vision KTH Royal Institute of Technology, School of Engineering Sciences Job description The Department of Mathematics at KTH welcomes applications for a
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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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, machine learning, and pattern recognition. The successful applicant will have: very strong programming skills and experience with deep learning libraries; strong python and object-oriented programming
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l'institut du thorax, INSERM, CNRS, Nantes Université | Nantes, Pays de la Loire | France | 3 months ago
using deep learning approaches. Effectively, in order to prevent cerebrovascular accidents, it is important to study the blood flow at the major bifurcations along the cerebral vascular tree (we mostly
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At the Technical Faculty of IT and Design, Department of Sustainability and Planning (PLAN), a Postdoc position in Satellite Data Processing and Machine/Deep Learning is open for appointment from
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strong computational–experimental feedback loop central to the project. Subject description Recent breakthroughs in deep learning–powered protein design, recognized by the 2024 Nobel Prize in Chemistry
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methodologies for AI based approaches to 3D mapping and visualisation in automotive scenarios. The focus of the project is to combine deep learning, dense/sparse visual SLAM, and neural rendering techniques
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for candidates with a strong computer science background, such as algorithms, machine learning and data science. Key Responsibilities Develop, implement, and evaluate machine learning and deep learning models
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg