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computational models with the "exact" but lower resolution information available from experiments. Job description: Application of specially developed approaches to define for transferable force-fields with
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is using state of the art machine learning tools to extract interpretable latent dynamics. We seek a highly motivated PhD student to develop a predictive computational model using recurrent neural
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), and computational modeling (deep neural networks). We apply multivariate analysis methods (machine learning, representational similarity analysis) and encoding models. Job description: This is an open
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part of Helmholtz networks, e.g., HIRSE; https://www.helmholtz-hirse.de and internationally Your Profile: University degree (Master, Diploma) in natural sciences, computer science or engineering PhD in
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work on molecular dynamics simulations, where molecular interactions are predicted by neural network potentials. These state-of-the-art neural network models promise simulations at unprecedented accuracy
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computational models with the "exact" but lower resolution information available from experiments. Job description: - Research and teaching is done on statistical physics and machine learning in physics
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the research area of adversarial robustness in LLMs as a Doctoral candidate / PhD Student (f/m/d) At the chair of Data Analytics and Machine Learning at the Technical University of Munich (TUM), a full position
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available for computational equipment and conference travel expenses. How to apply? Please send your application in English by e-mail to info.mmfm@mw.tum.de with the subject “PhD Application”. The application
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of ML for Earth observation will be supported. Requirements: PhD in computer science, geoinformatics, data science, business administration, or comparable field of study professional experience in Earth