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are part of a sub-project on Algorithmic Sustainability in association with Professor Christina Lioma and her Machine Learning research team in the Department of Computer Science at the University
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of the following areas: Research and development within computer vision and machine learning. Research and development within UAS platforms, subsystems, and payloads. Software design and development (C, C++, Python
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motivated researcher with: Strong background in control and optimization, preferably with experience in model predictive control (MPC). Solid skills in machine learning algorithms and data analysis
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, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit-learn, PyTorch) or physics-informed neural networks for thermal systems is a plus
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, Denmark, invites you to apply for a 7-month Research Assistant position funded by the IFD project “Cyber-physical systems for machines and structures – CP-SENS”. Expected start date and duration of
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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programmers. You will also work closely together with course teams to develop data generation, data analysis, modeling, simulation, and machine learning workflows as well as develop custom data science-related
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, TESPy, or similar libraries. Strong programming skills in Python or MATLAB, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit
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testing and documentation). Understanding of machine learning or statistical modelling as applied to strain design is an advantage. Strong communication skills and the ability to collaborate across
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Job Description Are you passionate about renewable energy and eager to apply machine learning to real-world challenges? Join our research team at DTU and work on groundbreaking advancements in