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Field
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modelling. MISSION You will actively contribute to the development and evaluation of new hybrid computational method to predict biological tissue deformation with subject-specific material properties
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to work on the use of hyperspectral data to explain and predict soil functions and communities in European mountains. We are looking for a candidate who has a very good command of artificial intelligence
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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development of new computational and mathematical models to quantify and predict infectious disease risk, particularly for identifying high risk individuals and groups. The PDRA will translate conceptual
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environment. Development of models to diagnose and predict battery performance and ageing. Participation in national and international research projects related with energy storage and its integration in
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fields). Strong quantitative skills and demonstrated expertise in predictive modeling and advanced computational methods (e.g., Multilevel Vector Autoregressive Models, Dynamic Structural Equation
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scientist. Job requirements Professional experience Machine learning / Deep learning tools (pytorch) and predictive modeling Bioinformatics analysis of omics data Education and training PhD or equivalent
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predict the location of resources more accurately, it is necessary to model these processes jointly at the basin scale. However, directly solving geochemical equations is computationally expensive and
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interest in social science applications, and with strong competence in statistics and machine learning. The successful candidate will develop predictive models using machine learning and work alongside other
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by investigating decomposition processes under controlled conditions to unravel the mechanisms of peat decomposition, identify the main actors and predict the interaction between these actors and their