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into the models for calibration (via PEST/PEST++) and for regionalization of results using machine learning algorithms. Field surveys in the Portuguese study areas will be required to maintain and upgrade
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datasets from laboratory experiments will be provided to support simulation and verification of the resulting model. Replicate and learn a theoretical model for wave and current interaction by posing
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, or behavioral data) and be proficient in Python and modern deep-learning frameworks (ideally PyTorch). Experience in computer vision, multimodal data fusion, self-supervised or generative modeling is highly
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engineering or related field At least 1 year of relevant experience in signal processing and machine learning. Good written and oral communication skills Proficiency in ANSYS, and lab test skill Ability to work
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of a call for awarding a research fellowship (RF) in the scope of the research project AQUALEARN – Machine learning-based digital twins for real time anomaly detection in water supply systems. 3
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: MSc degree completed. Additional optional skills and qualifications: Previous research experience, particularly in the fields of Internet of Things security and machine learning models applied
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and BSL-4 laboratories utilizing a wide range of techniques such as in vitro and in vivo infection models, flow cytometry, ELISA, western blots, and multiplex immunoassays. Our projects rely
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an advantage if you have Interest in the mechanical behavior of materials. Experience with machine learning and/or programming/coding. Experience with finite element modeling from civil, mechanical, or marine
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North America, to improve an existing model for the spread of Cyvirus cyprinidallo3 (also known as Cyprinid Herpes Virus or CyHV-3) as a biocontrol agent for common carp in Australia. The modelling will
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for model selection, validation, and workflow design, while more broadly contributing to a deeper understanding of what it means to trust unsupervised learning as an exploratory tool in scientific machine