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intelligence, computer algebra, computational logic and automatic reasoning Experience with SAT, SMT, QBF, MaxSAT Experience with algorithmic methods for commutative and noncommutative polynomials Experience in
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Description Implement and validate machine learning models and statistical algorithms for data imputation, anomaly detection and uncertainty management in geospatial environments. Collaborate in the preparation
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algorithms that allow robots to refine their control strategies based on observed human behaviour. Collaboration: The project will benefit from extending existing collaborations between the University
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programme Reference Number AE2025-0503 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0503
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for applications of research staff to cover contractual positions assigned to research projects, available at the following website address: https://s.ua.es/es/nlJx , approved by the Governing Council of 22 December
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(https://www.ise.fraunhofer.de/en/research-projects/pvev.html ), we are working to optimize models for PV self-consumption estimation and, on that basis, to develop an algorithm for PV feed-in upscaling
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professors, 2 postdoc researchers, and about 20 PhD students. The research for these PhD positions will be conducted in the System Software team, headed by prof. Bjorn De Sutter (https://users.elis.ugent.be
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academic research lab environment -Software programming for scientific simulations -Interdisciplinary and multi-physics relevant strong background -Design of computational algorithms for 1st /2nd order time
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formula is true or false (EXPTIME vs NP). Can we develop and implement efficient algorithms for this problem? This problem has been attacked using multiple different methods for the past 40 years, without
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Science and Services (TESS) lab (https://tess-lab.org/ ) in the Department of Geography, Streatham Campus, Exeter. Funded by the Saudi NEOM project, eligible students would receive Home tuition fees, a