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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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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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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
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, C/C++ and/or Java, etc.; experience with the implementation of specialized transport modelling software, optimization algorithms and procedures; strong ability and desire to learn new programming
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highly motivated doctoral student to join an ambitious project aimed at building machine and deep learning models to study the genetics of human disease. Funded as part of the Helmholtz AI program, the
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systems using various tools and models, including: i) characterization of the emerging patterns in physical systems (solid state materials and active systems); ii) investigation of the mechanical properties
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PhD position in interpretable machine learning for dementia prediction. The project focuses on developing interpretable deep learning models for dementia prediction using multi-modal data, including MRI
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Your Job: As part of an interdisciplinary team, you will develop approaches for the automated and large-scale provision and integration of energy systems data and models and apply data science
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architectures, capable of capturing the structure of complex, high-resolution NMR spectra – analogous to how language models such as ChatGPT learn the structure of human language. One of the primary goals is to
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analysis of large data sets, statistical modeling, and knowledge of at least one programming language (e. g.: R, Python and/or Julia) are required. Experience in machine learning and image recognition