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' to develop neural networks with remarkable information content: flies, which we use as a model, have brains that compute flying in 3D, navigation, metabolism and advanced learning and memory capabilities - all
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research. Your position Design, implement, and evaluate large-language-model (LLM) pipelines for synthetic data (fine-tuning, retrieval-augmented generation [RAG], prompt engineering). Plan and analyze
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material property database for composites. The candidate will utilize the database to develop AI models for composite discovery. The candidate will work with a multidisciplinary team to set up finite element
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twenty years have required the development of more generic codes capable of accounting for certain specificities such as continuous fuel recycling or the modeling of effects associated with the migration
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Postdoctoral Position in Probabilistic Machine Learning for Spatio-Temporal Data Modelling A postdoctoral position is available at the Department of Computer Science, Aalborg University Copenhagen
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and numerical models as well as constitutive model calibration and validation based on physical experimental data. Required Qualifications: A successful applicant must have a PhD in Engineering
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atmospheric modelling. You enjoy teamwork in an international setting and can also work independently to move projects forward. You also possess: A PhD in atmospheric sciences, physics, remote sensing, or a
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statistical and mathematical modeling. PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining. • Research in mathematical
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. Disseminate outcomes through journal papers, conference contributions, and accessible briefs for stakeholders and workshops. Mentor and coordinate with ECRs/MSc/PhD contributors on methods, modelling, and
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candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models for polymer-based materials. This project aims to leverage