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manufacturing technologies and eager to develop and build experimental setups and combine this with physics-based modelling? Join us as a PhD candidate and contribute to making volumetric 3D printing predictable
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attention to scientific rigor and interpretability Experience with XAI tools (SHAP, LIME, Integrated Gradients) to identify which features of the model are driving the predictions Clear written and verbal
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sustainability. The selected researcher will contribute to the development of predictive models and machine learning algorithms for data analysis from plant-based sensors, multispectral and thermal imagery, and
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George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș | Romania | 14 days ago
intelligence-based prediction models in healthcare: a scoping review. npj Digit. Med. 2022, 5:2. https://doi.org/10.1038/s41746-021-00549-7 Hassan, N., Slight, R., Morgan, G, Bates, D.W., Gallier, S., Sapey, E
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exploratory analysis on large, multi-dimensional datasets; (b) develop predictive/diagnostic models and algorithms to lead and support clinical/translational research; (c) collaborate with cross-functional
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, predictive analysis and immersive interactions, among others. Where to apply Website https://www.poliba.it/it Requirements Additional Information Eligibility criteria TITLES AND INTERVIEW Eligible destination
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research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. The Research Engineer (Modelling and
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data-model integration, leveraging the U.S. Department of Energy’s (DOE) Leadership-Class Computing Facilities to advance predictive understanding of complex environmental systems. Major Duties
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techniques for analyzing data collected from hardware sensors installed on electrospindles and the development of models for calculating the spindle’s remaining useful life. The contractor will explore topics
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are crucial for weather and climate predictions, rain formation, and the water cycle. According to the Inter-governmental Panel on Climate Change (IPCC), cloud modelling is one of the least understood domains