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Field
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of the Grant are:; 1) To apply machine learning algorithms for the diagnosis of faults and malfunctions in photovoltaic plants, using data from SCADA systems combined with synthetic data from digital twins (DT
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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Experience and practical knowledge of programming languages and tools (e.g. Python, Java, etc.) Knowledge in Software Engineering, AI, machine learning is an advantage Experience with software observability
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incorporate optimized machine learning algorithms, support standardized IoT protocols, and be validated in laboratory and semi-industrial environments. The project contributes to smart maintenance strategies
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Computational Mathematics for reliable and trustworthy uncertainty quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a
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artificial intelligence, computer science, engineering, mathematics, physics, or a related discipline Demonstratable background in machine learning, information retrieval or natural language processing
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well as the programmes in statistics, cognitive science and innovative programming. Read more at https://liu.se/ida The position is based at the Division of Statistics and Machine Learning (STIMA). We conduct research
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 28 days ago
the Medical University of Vienna, the Technical University of Vienna and University of Vienna, the AITHYRA and CeMM PhD programs for both life scientists and computational scientists/machine learning experts
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scientific coding skills in Python. You are strongly motivated to acquire advanced skills in Python and Fortran and in the use of high-performance computer systems you have affinity and preferably experience
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the project, supported by Dr. Adamo and close collaboration partners, within an environment that encourages academic freedom and scientific independence. In line with our and Uni Basel values (https