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Field
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algorithms, computational complexity theory, and information theory Relevant coursework and experience in spiking neural networks, and statistics A strong electronics background, including experience in
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Develop solutions to integrate large foundation models
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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candidate with a background in SAR/InSAR signal processing and time series algorithms, combined with strong expertise from an application domain that strengthens the group’s current activities. Key
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period of 12 months, possibly renewable up to a maximum of 36 months, scheduled to start on March 2026. 2. WORK PLAN AND WORKPLACE: The project will investigate the developed algorithms and methods
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on the complementary expertise and recognized excellence of its 22 research teams to contribute to the development of the fundamental aspects of computer science (models, languages, methods, algorithms) and to foster
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a PhD student, you will develop state-of-the-art learning and inference methods to detect and characterize anomalous radio behavior and to design algorithms that remain reliable under practical
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, under the scope of a term contract, with a termination date undetermined, overseen by Portuguese Labour Code. Job description: Contribution in WP3 Task 3.3 aiming to define compressed sensing algorithm
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communication skills, and a keen interest in conducting impactful research. For details on how to apply, please see here: https://www.centre-ub.org/studentships/application-process/ Interviews for this studentship