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Field
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. You will work on the cutting edge of both wind energy and machine learning, two of the fastest growing scientific disciplines, to develop machine learning surrogates of wind energy systems. As newer
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techniques for effective analysis of massive-size geophysical data. The goal is to develop algorithms for classification and predictions that enable early warning systems in various geosciences applications
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Stig Brøndbo 14th April 2025 Languages English English English Faculty of Science and Technology PhD fellow in “LLMs based Knowledge Graphs towards Digital Twin Development in Green Shipping" Apply
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skills and be interested in developing a collaborative program of applied research in robotics. For example, this may include sensor development, applied robotic perception, algorithm development, or other
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and polyploid crop species and benchmark them against other methods such as graph-based methods. This project will combine algorithm development and computational programming with large population
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Sustainable Energy AS. Duties of the position The technical work tasks concern: Development of smart algorithms and modules for load prediction and minimization of fuel, energy, and emissions for marine vessels
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data on homeowner retrofit needs and preferences. Undertaking research trials to test and refine the AI algorithms used in our platform. Meaningful assistance in research and policy development with a
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and the role of key architecture components can lead to the development of more efficient and robust training algorithms. This can ultimately result in AI systems that are both more powerful and
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solutions using first motion. Experience in full waveform inversion using innovative tools (e.g. ISOLA) and methods (ML), earthquake location algorithms, computer programming and geophysical equipment
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development over the last two decades. This research topic aims to define novel approaches to developing and combining these intelligences, utilizing both 1st and 2nd wave AI approaches, in the context