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on developing a new multi-disorder prediction approach that integrates different sources of information. You work with analytical model development, extensive simulation studies and analysis of existing large
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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 Rahimpour
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be defined at two levels: SAACD Component: This is a UAV made up of hardware and software sub-systems, capable of observing, predicting, deciding and reconfiguring itself to fulfil its mission (e.g
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models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to rupture, which is one cause of a stroke and thus the prediction of plaque rupture is very
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materials databases to be integrated into the NIST-JARVIS (https://jarvis.nist.gov/ ) infrastructure. We work closely with experimental collaborators for validation and focus on releasing software, models
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, predictive analysis and immersive interactions, among others. Where to apply Website https://www.poliba.it/it Requirements Additional Information Eligibility criteria TITLES AND INTERVIEW Eligible destination
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Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning
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(NKFIH) of Hungary within the National Research Excellence Program (NKKP) ADVANCED project entitled "Development of Predictive Scanning Tunneling Microscopy and Spectroscopy Simulation Methods for Novel
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manufacturing technologies and eager to develop and build experimental setups and combine this with physics-based modelling? Join us as a PhD candidate and contribute to making volumetric 3D printing predictable
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George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș | Romania | 21 days ago
intelligence-based prediction models in healthcare: a scoping review. npj Digit. Med. 2022, 5:2. https://doi.org/10.1038/s41746-021-00549-7 Hassan, N., Slight, R., Morgan, G, Bates, D.W., Gallier, S., Sapey, E