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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
; - Collaborate in the writing of technical reports regarding the protocols, mechanisms, and algorithms developed; - Co-author scientific publications based on the work developed; - Prepare the research grant
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mindset and intellectual curiosity to strengthen and complement the research profile of the Mathematical Insights into Algorithms for Optimization (MIAO) group at the Department of Computer Science at Lund
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required. Expertise in optimization or efficient algorithm design will be considered an asset. Applications should include a CV, a list of publications and a research statement. Applicants should also
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exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep learning models, which find application in image-based science, including in
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to research, develop, and evaluate algorithms for a variety of geospatial, signal, and/or image processing applications. The successful candidate will have a solid background in computer science with strong
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the potential for significant speedup in computations. Job responsibilities Research and Development: Conduct research to develop novel algorithms and methodologies for tensor completion in multidimensional
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with strong experience in learning-based control and mobile manipulation. The successful candidate will work on algorithm development for contact-rich robotic manipulation, integrating perception
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inference in engineered systems, including telecom networks; The development of neuromorphic algorithms and spiking neural models with built-in efficiency and reliability guarantees; The design of reliability
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of precipitation and temperature over Morocco. Key Responsibilities: Statistical model development: Led the development of advanced statistical models and machine learning algorithms for forecasting precipitation
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Your Job: Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep