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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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Application deadline: 30/04/2026 Research theme: Nuclear Engineering How to apply: https://uom.link/pgr-apply-2425 This 3.5-year PhD project is fully funded; home students are eligible to apply
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, nonlinear dynamical systems, robotics, and formal methods to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will
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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The research program aims to develop new sensing algorithms integrated
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processing algorithms for joint communication and sensing. Analyze experimental datasets, extract statistical models, and compare findings across environments, hardware, and frequency bands. Collaborate with
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, Effects, and Criticality Analysis (FMECA), functional FMECA, advanced sensing techniques, sensor and operational data fusion, data analytics, and machine learning algorithms for condition monitoring, fault
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growth of the Illinois Tech Mumbai academic programs Special consideration will be given to candidates with teaching or research interests in: Algorithms Science of Programming Computer Vision Machine
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(http://vanallenlab.dana-farber.org/) to work on the analysis of new datasets generated in the context of multiple clinically oriented cancer sequencing projects in order help advance efforts
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reproducible research practices. Your responsibilities Develop and implement computer vision and image processing algorithms for star tracking and satellite detec-tion using event cameras. Design and build a
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, transfer learning, federated learning, data integration, algorithmic fairness, survival analysis, and methods for heterogeneous and multi-source data. Training Environment and Career Development