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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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of what microscopes can achieve. You will create and apply sophisticated algorithms, physics-based simulations, and machine learning models to process complex data from our cutting-edge imaging systems
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are post-docs, post-graduates or apprentices. Altogether, PSI employs 2300 people. For being part of the Soft Matter Group, in the Laboratory of Neutron Scattering and Imaging LNS at SINQ, we are looking
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that reproduces, in a web environment, the visualisation and annotation interfaces defined in the project; • Ingestion, cleaning and storage pipelines (image, depth, metadata) with continuous integration testing
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web frameworks, REST/GraphQL APIs, automated testing). • Continuous integration and continuous delivery (CI/CD) in a cloud environment. • Functional validation and performance of the prototype in a
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for the development of the prototype should be entirely synthetic. For this purpose, generative artificial intelligence tools (5)(GenAI) should be used to create users with different profiles and relevant datasets
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and functional requirements of the system, analysis of data sources and characteristics, definition of performance metrics and criteria for comparison between approaches. 3) Initial prototype
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-weighted imaging, and EEG, in combination with genetic data (GWAS, bioinformatics) from large-scale population cohorts. Positioned at the intersection of biological psychology, cognitive neuroscience, and
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Support”, through analysis of MR Diffusion Weighted Imaging and MR Spectroscopy and leading other members of the wider team Specific aims of the project To prospectively evaluate non-invasive pre-surgical