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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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with .root files and explore different event generators as well as machine learning tools and algorithms. The nature of LDMX as an international project will require you to work collaboratively in
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knowledge in thermal energy storage, techno-economic analyses, and AI tools/algorithms is a merit. In addition to the above, there is also a mandatory requirement for English equivalent to English B/6
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). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction solvers. Computational aeroacoustics. Swedish is not required
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of computational fluid dynamics (CFD). Knowledge of finite element method (FEM). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction
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communication systems for 5G and 6G, channel modelling, channel characterization, radio-based positioning and sensing, and associated signal processing algorithms. Work duties Employment as an assistant professor
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experience of application of artificial intelligence including machine learning and deep learning algorithms. Documented programming skills in Python, R, or MATLAB. Very good knowledge of English, spoken and
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with machine learning and generative AI algorithms, with working knowledge of deep learning frameworks such as PyTorch or TensorFlow is considered a strong advantage. • Extensive experience in multi
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interdisciplinary research. Systems development may include, for instance: advanced materials, surfaces and fabrication; distributed sensors and actuators; quantum and photonic systems and interactions
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division are on technologies with applications that span from basic science to future sensors and communication systems. We are at the forefront of innovation in terahertz electronics, working on projects