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, the fusion community has started to develop fast surrogate models based on Machine Learning / AI models to speed up significantly the employed tools. Such tools have demonstrated to be generally applicable and
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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology
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mechanics, finite element modeling, and scientific machine learning. The RSE will contribute to the design, implementation, and maintenance of open-source software libraries that integrate phenomenological
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, and/or multiphysics modelling • Mathematics & AI: Numerical analysis, inverse problems, neural networks, scientific machine learning • Programming: Python (scientific computing, ML), preferably C
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Infrastructure? No Offer Description Work group: IAS-8 - Datenanalyik und Maschinenlernen Area of research: PHD Thesis Job description: Your Job: We are looking for a PhD student in machine learning to work within
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. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
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of visualisation, machine learning, and human-computer interaction under the joint supervision of both institutions. The position is shared by TU Wien and USTP and offers the opportunity to conduct research at both
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(pre-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models; integration and analysis
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like knowledge graphs, information extraction, machine learning or artificial intelligence in the context of scholarly data Publication of research and project results Your profile: Scientific University
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into actionable insights, novel tools, and impactful research outcomes. Key Responsibilities Develop, implement, and optimise AI/ML models (artificial intelligence/classical machine learning, deep learning