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models. The scientist will conduct research using machine learning and classical parameterization methods on data from ocean gliders equipped with microstructure turbulence sensors, turbulence resolving
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enhanced MRI with computer simulations of image contrast and mass spectrometric imaging of tissue samples and single cells. This project is part of the Collaborative Research Centre 1450 “Insight
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the duration of their PhD, plus periodical monitoring and evaluation activities. The objective is to provide students with all the support they need for the proper development of their research career. The ICN2
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reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl
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the controlled flow at tunable temperature and photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning
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at the below link: http://www.medschool.umaryland.edu/Anesthesiology/Research/ Expected rank is Assistant Professor or higher; however, rank and tenure status is dependent on candidate's qualifications
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the first call lasts from the 1st of July to 31st of August 2025. Description of specific PhD projects: Machine Learning Interatomic Potentials for Chemical Reactions Hosting: Tallinn University of Technology
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information systems engineering. The group conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, Machine Learning/AI, GenAI, and IoT/5G
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. This comprises learning to set up and operate our new optical cryostat platform, which involves advanced confocal microscopy, laser pulse shaping, and time-bin interferometry. (b) You will benefit from our
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technologies for real-time health monitoring and diagnostics. About UCAM-SENS UCAM-SENS is a top-tier research unit dedicated to advancing (bio)electroanalytical sensing for digital transformation in healthcare