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strengthen the data science and machine learning activities of the IAS-9 with exciting new topics. You will work in a multidisciplinary team of enthusiastic data scientists, software developers and domain
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machine learning (ML), artificial intelligence (AI) or related fields • Software skills in ML languages such as Python • Ability and enthusiasm to learn new technologies quickly • Ability to work highly
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor extraction and micromechanical simulations (MCRpy, DAMASK) Vary the material processing parameters, which
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measurements in a team of experts on and in the pyramids and creating digital object models with numerical simulations, for example, using Salvus software or similar. Publication of research results and
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exciting new topics. You will work in a multidisciplinary team of enthusiastic data scientists, software developers and domain scientists on, e.g.: Developing self-supervised learning frameworks to extract
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+C). You will extend the existing core-level GW+C implementation in the FHI-aims software package. Currently, this approach is limited to molecules; your task will be to adapt and expand it for
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Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor extraction and micromechanical simulations (MCRpy, DAMASK) Vary the material processing parameters, which
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analysis and analytical data analysis workflows, together with other team members Implementing AI-based microscopy image analysis software as python packages Developing algorithms to deploy machine learning
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engineering, structural dynamics, basic isolation, finite element modelling. Proficiency and/or interest in programming languages (e.g. MATLAB, Python, R) and software platforms such as OpenSees, ABAQUS, LS