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interested in unique interdisciplinary research, integrating molecular simulations, machine learning, statistical physics, multiscale modeling, and uncertainty quantification. The successful applicant will
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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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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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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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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machine learning methods based on specific problems The targeted visualization of results is also within your area of responsibility What you bring to the table You are studying physics, mathematics
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team and actively participate in the DIPONI project (“Digital Transformation in Polymer Processing: Interoperability and Machine Learning Solutions for Process Optimization and Sustainability
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. Specifically, the PhD candidate is expected to contribute corpora preparation (collection and organizing the annotation), use machine learning approaches for irony detection, and testing for experimental and
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Biology, Computer Science or related studies) Experience in Python with PyTorch (or equivalent) programming Experience in sequencing data analysis Basic knowledge in machine learning Experience with linux
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image