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applications, agricultural monitoring and modeling, Agro-AI/ML, or digital twin. Instructions to Applicants: For full consideration, applicants must apply for the Research Assistant Professor at https
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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16th March 2026 Languages English English English The Department of Structural Engineering has two vacancies for SFI FAST: PhD positions in Modelling Strength and Failure in Recycled Aluminium
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 7 days ago
Europe Research and Innovation Programme under the Grant Agreement No.101178775 Workplan: Design, develop and implement deep learning methodologies for generation of subsurface earth models. Duration
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, public authorities in their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? You will develop a multi
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behavior of pests, their response to pesticides, including physiological and genetic mechanisms of resistance, innovations in applied pest management research (e.g., molecular, computational modeling
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and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed
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sponsorship Salary: Salary commensurate with education and experience Restricted: Yes Criminal Background Check: Yes About the Department: The Center for Spatial Information Science and Systems (CSISS) is an
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conditions corresponding to the natural geological environment to establish a model for the potential hydrogen resource in terms of volume and generation rate. Specifically, you will be responsible
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mechanistic process models with machine learning for accuracy, generalization, and interpretability. Uncertainty-aware AI: robust inference under noise, drift, and changing conditions; knowing when a model is