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SyMulDaM project involving the development of predictive models to quantify the integrity and durability of a nuclear power plant containment structure., within the mechanical engineering department
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, and artificial intelligence to develop predictive models and computational tools that guide the design of new antibiotics. The postholder will contribute to the development of predictive and generative
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Research Associate to develop and apply new modelling approaches to understand how development shapes population dynamics under environmental change. The role sits within a NERC-funded project on development
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to environmental change. The role will focus on generating high-quality data to inform and validate modelling work within a NERC-funded interdisciplinary project. The NERC-funded project develops a new development
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data to inform and validate modelling work within a NERC-funded interdisciplinary project. The NERC-funded project develops a new development-centric framework for understanding adaptation under
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
predictions for digital‑twin applications. Your responsibilities include: Development and implementation of thermo‑mechanical models in FEniCSx Integration of FEM models into neural operators and PINNs
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Institut de Recherche pour le Développement (IRD) | Montpellier, Languedoc Roussillon | France | 4 days ago
these datasets, build models of carbon stock dynamics in ecosystems. Your work will contribute specifically to objective (2), focusing on quantitative IR spectroscopy. Over the past thirty years, many research
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Asia (SEA), focusing on Manila (Philippines) and Ho Chi Minh City (Vietnam). This position will be based at the School of Engineering at Newcastle University, focusing on developing models to predict AMR
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primary supervision of Dr Thomas Ouldridge. The student will develop predictive models of nucleic acid strand displacement rates to allow the rational design of complex networks of ever-increasing
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AI4TECS aims to develop the first AI‑powerAd system that integrates real‑time EC identification using non-target high resolution mass spectrometry data, toxicity prediction, and transformation modelling