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- Maastricht University (UM); 16 Oct ’25 published
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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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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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resilience. This task will involve integrating demographic models (e.g., structured population like matrix population models and/or integral projection models) with community stability analyses using, among
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, responsible, and organized postdoctoral researcher to develop mathematical models of adaptive stress regulation. These models will inform empirical research with humans. Join our team! We invite applications
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interest in epidemiology and should hold a PhD in Epidemiology, Phytopathology, Mathematical modelling, or a similar subject. Candidates must have either proven expertise in theoretical epidemiology
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submitted by the reference writers via http://www.mathjobs.org/jobs , addressed to: Chair, Statistics Search Committee, Department of Mathematics and Statistics, Mississippi State University, Mississippi
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hold a PhD in computational biology or a related field and are motivated by understanding biological systems through computational methods. Experienced modeller – You have demonstrable skills in
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Knowledge of biogeochemical cycles Experience with process-oriented modeling Selection Criteria PhD degree or Master degree in a field such environmental sciences, agronomy, applied mathematics
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Mathematics (ICTEAM, division of Applied Mathematics, https://uclouvain.be/en/research-institutes/icteam , Euler building, 4 avenue Georges Lemaître, 1448 Louvain-la-Neuve) Institute of Neuroscience (IoNS
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on the development of the SPHEREx Legacy Galaxy Clusters Catalog. The successful candidate will lead analyses to characterize galaxy populations in clusters using SPHEREx data in combination with complementary wide