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French National Research Institute for Agriculture, Food, and the Environment (INRAE) | Montpellier, Languedoc Roussillon | France | about 2 months ago
with skills in statistics and modelling through machine learning (Agronomy joint research unit, MIA joint research unit, Wageningen University & Research); (ii) national and international laboratories
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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML
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: “Towards a trustworthy strategic use of data in machine learning pipelines”. CUP: D53C25002380001. Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079
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Bridging Causality and Forecasting in Space Weather: A Transparent Complex Systems Modelling and Interpretable Machine Learning Framework School of Electrical and Electronic Engineering PhD Research
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About the Role The Learning Designer will support academic and teaching staff to design, develop and deliver transformative curriculum in new and amended programs. The Learning Designer will work
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Position Details Position Information Recruitment/Posting Title Supervising Mental Health Specialist (Military/Veteran) Job Category Staff & Executive - Healthcare Department UBHC - Military
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Machine Learning Methods for Enhancing Autonomy of Unmanned Aerial Vehicles in Wildfire Detection and Localisation School of Electrical and Electronic Engineering PhD Research Project Self Funded
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Digital Education. If you're passionate about leveraging digital technology to enhance learning experiences and eager to gain professional experience in a dynamic environment, we invite you to join us as a
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of Business Intelligence (machine learning & LLM) to enhance the sustainability of regional tourism. On the supply side, a monitoring system is developed by extracting data from online platforms such as Google
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RAP opportunity at National Institute of Standards and Technology NIST Scientific machine learning methods for trustable accelerated materials characterization and design Location Material