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About us: We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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computational approaches to uncover novel biomarkers and therapeutic strategies for CNS disorders. Key Responsibilities: Develop and implement algorithms for multimodal image fusion, combining data from MRI, PET
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forms of professional diagnosis, inference, and treatment in the financial sector, including taxation and insurance, are changing as professionals work more with algorithms. The two advertised positions
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analysed by bespoke machine-learning driven algorithms, combined with physical models, to de-noise images, identify features and correlate properties, giving critical insights into power loss pathways
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Location: Luxembourg, Luxembourg 1511, Luxembourg [map ] Subject Area: Quantum LDPC codes with efficient decoding algorithms Appl Deadline: none (posted 2025/08/05) Position Description: Apply Position
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scale. Test methods for calibration corrections and noise reduction in retrieved data. Test algorithms for retrieval of sea surface temperatures from infrared radiances. Test algorithms for cloud masking
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imagery). Experience in building data models using Python or other statistical and/or mathematical programming packages. Proficiency in developing machine learning algorithms to analyze spatial-temporal
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learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms at the intersection of robot learning, control, and AI
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(“Autonomous System for Hybrid Hyperspectral-SAR Monitoring in Precision Agriculture”, Supervisor Prof. Hugo Hernández Figueroa, and “Development of Methodology and Robust Operational Algorithms for Hybrid
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methods to improve the deployment, adaptation capabilities and safety of robots and critical infrastructures. The developed algorithms will be evaluated on legged robots, wheel-based robots and under