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on other aspects of the mechanical and multiphysics behavior of heterogeneous materials. Where to apply Website https://www.academictransfer.com/en/jobs/356990/phd-on-ml-accelerated-simulatio… Requirements
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. Minimum Qualifications Applicants should have a PhD degree in data science, computer science, computational sciences, applied mathematics, statistics, or a related field. Degree must be conferred prior
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Arts et Métiers Institute of Technology (ENSAM) | Paris 15, le de France | France | about 2 months ago
discovering research and potentially pursuing a PhD. Expected skills • Solid background in numerical methods (PDEs, finite elements, scientific computing). • Interest in modeling, model order reduction, and
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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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Spacetime emergence and cosmological dynamics in group field theory School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Steffen Gielen Application Deadline: Applications
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economic assessments machine learning or proxy-model based methods field scale simulation geological features geomechanics reactive flow The PhD fellow are not expected to master all these topics. Project
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 21-Dec-25 Location: San Jose, California Type: Part-time Categories: Academic/Faculty Mathematics/Statistics Internal
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? No Offer Description We have two exciting PhD positions at the intersection of formal software verification and Large Language Model (LLM) safety, focusing on extending state-of-the-art logic-based
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, architectural engineering, (applied) physics, applied mathematics, or a related field is required. Strong mathematical and analytical skills are essential. Solid understanding of numerical modeling and signal
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modeling techniques. Advanced knowledge of R, Python, or other relevant programming languages. Ability to use/learn several advanced modeling methods (e.g., statistical, mathematical, individual-based