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Website https://www.academictransfer.com/en/jobs/357006/phd-in-computational-modelling-… Requirements Additional Information Website for additional job details https://www.academictransfer.com/357006/ Work
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Modelling (WCPM) . Find out more about the research group at https://koczorbenda.wordpress.com/ Requirements and eligibility: Applicants must have, or be predicted to obtain, a good degree (2.1 or 1st class
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. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum
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/Qualifications - Automation or applied mathematics background, with a strong interest in physical models and numerical method - Analysis of partial differential equations, variational approach, Bayesian estimation
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, biology, physics, mathematics, engineering or related fields. Qualifications and Education Level: Advanced Vocational Training (CFGS/FPII), Bachelor’s degree, Master’s degree or PhD. Skills: – Programming
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at the University of Southampton: https://software-oasis.com/ Our group is developing an innovative class of 3D climate models to explore the atmospheres and climates of Earth and other planets across the Solar
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) Sworn statement declaring that the applicant has not completed the maximum legal duration of employment allowed for this category, using the model available at: https://www.ulpgc.es/spersonal
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to excellence in research, teaching, and citizenship. You will be able to demonstrate: A PhD in the Mathematical Sciences or in a closely related field, or equivalent. A strong research record with publications
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, are reshaping them. As a PhD candidate you will contribute to this research project by developing and applying AI and Data Science techniques, with a focus on causal modeling and causal learning, to investigate
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related to staff position within a Research Infrastructure? No Offer Description The Mathematical Institute is looking for a PhD candidate in Statistics to join the NWO project “DynoSurv: flexible