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, develop theory and algorithms for their practical use, and study complexity and performance trade-offs in relevant applications. The project is led by Professor Erik Agrell (IEEE Fellow), whose
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that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance and edge computing; The design of architectures
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pipeline to evaluate the accuracy of deep learning algorithms. Apply deep learning models to analyze large-scale molecular and cellular datasets related to high risk model of schizophrenia generated in our
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. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum
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, develop, and implement state-of-the-art post-quantum cryptographic protocols and algorithms. Collaborate closely with top-tier European universities and industrial partners. Participate in project
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Operations Manager to lead and monitor operations for continuous algorithm improvement and validation. This will involve guiding the development of data management systems and establishing data assessment
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; (ii) teaching university-level computer science courses in the following subjects: Data Structures, Algorithms, Operating Systems; Formal Languages and Automata, and Network Security; (iii) advising
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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep
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/Programming skills are essential to complete this project through geospatial modeling automation, image fusion algorithm development with advanced STARFM process, quality peer-reviewed publications, etc
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treatments for mental illness. To this end, we bridge computational models that target various levels of analysis, including the algorithms (e.g., reinforcement learning models) and their neural