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Field
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Patient-derived organoids are the hallmark of personalized medicine with seemingly endless possibilities to match the right treatment to the right patient. As organoid data are often longitudinal in
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on developing advanced machine learning models to quantify phenotypic traits of crops, including corn, soybean, and other selected species. These models will leverage data collected from various sources, such as
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Research Infrastructure? No Offer Description We are looking for a researcher to work on the finite element modelling of the interaction between textile materials (yarns and fabrics) and textile
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Your job Are you fascinated by using mathematics to solve fundamental questions in ecology? Do you thrive on translating abstract models into biological insight? If so, this PhD opportunity could be
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Infrastructure? No Offer Description Are you fascinated by using mathematics to solve fundamental questions in ecology? Do you thrive on translating abstract models into biological insight? If so, this PhD
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Description Two PhD positions in atmospheric selenium research: field & lab analyses and process-based modeling We are seeking two highly motivated and curious PhD students to join the Inorganic Environmental
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medicine with seemingly endless possibilities to match the right treatment to the right patient. As organoid data are often longitudinal in nature (e.g. expressed by growth curves), can be derived from
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 1 month ago
this thesis, we will focus on the models expressed by logics and data. These models are used in order to check different properties about these models in order to check that the models are correct and coherent
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Energy Districts through citizen-centered socio-technical models for upscaling of the heat transition – seeks to tackle this challenge by involving key stakeholders (citizen groups, municipalities, housing
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will contribute to projects focused on developing advanced machine learning models to quantify phenotypic traits of crops, including corn, soybean, and other selected species. These models will leverage