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We are seeking a highly motivated researcher to conduct research in modelling and monitoring forest growth. About the position The projects focus on developing models for forest types and tree
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excursions. The PhD student will work on analysing the reliability of paleomagnetic records, deriving uncertainty estimates, and building time-dependent models of the paleomagnetic field evolution using a
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offered in this context, with the objective of modelling, coding, and field-validating a new mechanistic analysis tool for pavements containing fungal-bound granular layers. The research will focus on urban
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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PhD: Semantic Modelling of Geodata Sources and Geo-analytical Workflows Faculty: Faculty of Geosciences Department: Department of Human Geography and Spatial Planning Hours per week: 36 to 40
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, biochemists and technicians. The multidisciplinary research in our group covers a wide range of topics from radiobiology, radiation physics and space research to radiation therapy. The radiobiological modelling
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into Question 2 of the Financial Details section. About the scholarship This PhD project will contribute to the development and optimisation of a Paediatric Post-Sepsis Model of Care that supports and improves
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 13 hours ago
applications have been organized in different steps that interact with each other. The classical first step is to describe the application through a model. Through this model, a first process is operated
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Your job Are you a highly motivated and enthusiastic individual with a strong background in process-based modeling, data analysis, and soil sciences? Do you want to participate in a large-scale
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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with