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develop new communication theory and signal processing algorithms. The goal will be to develop theory, algorithms, and network architectural concepts to deliver ubiquitous network services across the globe
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the mathematical foundations of these fields, e.g., designing innovative algorithms and control strategies, as well as the development of technical solutions to adapt these new methods to applications in the areas
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learning, optimization algorithms, and interoperability frameworks for optimal energy management across Europe. KTH leads technological landscape analysis, multi-energy investment planning tool development
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. Supervision: Dr. Rafael Guedez, Prof. Björn Laumert and Dr. Silvia Trevisan are proposed to supervise the doctoral student. Decisions are made on admission What we offer The possibility to study in a dynamic
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significant role in learning in AI by enabling cognitive agents to acquire actively knowledge and skills through interaction with their surrounding environment. Embodied AI requires tools, algorithms, and
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mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. Experience in applying or developing machine learning
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corresponding knowledge in another way. A successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms
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theoretical research, algorithm design, and the development of software tools that demonstrate the applicability of the new methods. Research environment The positions are hosted by the Department
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transparent models. The Privacy-aware transparency decisions research group (led by Prof. Vicenç Torra) conducts research in data privacy for data to be used for machine and statistical learning. It is well
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modular, scalable, and transparent control algorithms suitable for real-time implementation across different vehicle platforms. - Contribute to theoretical developments in stochastic model predictive