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effort between School of Engineering and School of Chemical Engineering at Aalto University supported by our industrial partners. The PhD student to be jointly supervised by two supervisors; Prof. Hamid
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Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers
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maritime and autonomous. You will have a prior background in naval architecture, ocean engineering and mechanical engineering. You should be already familiar with the majority of simulation and experimental
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architectures. Application domains include, among others: meteorology, climatology, aeronautics, automotive, astrophysics, high-energy physics, materials science, energy production and management, biology, and
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of sedimentological concepts to reconstruct depositional history, paleogeography, and sedimentary architecture. Presentation of results at national and international conferences. Publication of approximately two peer
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engineering, and hyperparameter tuning to produce resilient and high-performing models. · PhD in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field Strong track record
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collaborative culture, NREL stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus
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-impact problems. The ideal candidate will have a strong grasp of diverse machine learning techniques and a passion for experimenting with model architectures, feature engineering, and hyperparameter tuning
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industry, the vast majority of ideas will fail. So, since inception, we’ve focused on continuous enhancement of our automated research platform and cutting-edge technology, allowing us to fail faster than
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majority of ideas will fail. So, since inception, we’ve focused on continuous enhancement of our automated research platform and cutting-edge technology, allowing us to fail faster than the day prior, glean