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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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technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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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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, or as soon as possible after that. The Department of Electrical and Computer Engineering is organized into sections. The position is anchored in the "Signal Processing and Machine Learning" section [1
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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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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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history analysis and possibly also with machine learning. Another specific example is statistical software development, e.g. implementing tools and algorithms for working with causal discovery