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sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT
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Job Description If you have solid practical experience in embedded systems, computer engineering, or related areas — and are excited to teach, collaborate, and shape the next generation of engineers
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(EoS), or machine learning approaches. Hands-on experience in extracting bioactive compounds from biomass. Strong collaboration skills and the ability to work effectively in interdisciplinary teams. A
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modelling, advanced machine learning tools, etc. We welcome applicants with a strong academic background within engineering or applied science, whose expertise supports the development of resilient and
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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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, the CAPeX approach to finding new electrocatalytic materials for energy conversion reactions uses state-of-the-art machine learning techniques, but experimental feedback is needed to improve the models and
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are looking for candidates who have experience with developing AI or machine learning models, as well as bacterial sequence analysis. You should be familiar with relevant programming languages such as Python
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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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activities, i.e., teaching and supervision of BSc and MSc student projects at DTU. We are looking for candidates with Strong skills in AI, Machine Learning, and/or Data Science, preferably with experience in
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness