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-flexible technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their
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) 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 of the assessment. All components assembled
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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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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
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested developing new machine learning methods for precision medicine and
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are looking for a highly motivated and skilled PhD researcher to work on structural surrogates of offshore wind foundations through graph-based machine learning. Our goal is to perform full-structure
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opportunity to learn, develop and apply a range of cutting-edge modeling and computational techniques. You will work in an interdisciplinary, cutting-edge, fast-paced research environment, interact with
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marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
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learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
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Experience in processing remote-sensing information and machine learning is an asset Capacity for interdisciplinary teamwork and excellent communication and presentation skills Ability to communicate in