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on the project can be found here: https://hecustom.eu/ This post will contribute to the creation and validation of a digital twin (with biological bone models) to assess and interrogate the issue of
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transcriptomics and multi‑omics data. You will also partner with AI experts to integrate predictive models and advanced analytics into omics workflows. You will work in an expanding team led by Dr. Masoomeh
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light–matter interaction through appropriate transport models, properly accounting for attenuation effects due to the materials. The activities will be carried out within the EIC PATHFINDER PREDICT
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leading international centre for developing computational models that support personalised, efficient, and predictive healthcare—advancing the future of medicine through digital transformation. More info
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Modeling Core is home to a consortium of postdoctoral fellows who provide modeling expertise for a wide range of projects as integral members of those research teams. Unit URL https://imci.uidaho.edu
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. The successful candidate will contribute to the development of predictive numerical models and experimentally validated designs for intricate mechanical assemblies, deployable structures, compliant mechanisms, and
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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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago
carbon-cycle modeling. The project will build a unified modeling framework that uses GEDI LiDAR and Landsat/HLS data to train deep learning models capable of predicting forest structure variables such as
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intelligence models for the analysis of multispectral remote sensing imagery. The main tasks include implementing computer vision and machine learning methods for the detection and prediction of algal blooms in
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background in nonlinear optics, ultrafast photonics, and integrated photonics, alongside the ability to develop predictive models for optical materials and photonic devices. The successful candidate will work