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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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for anomaly Detection and diagnostics: Leveraging state-of-the-art machine learning and deep learning models for automated fault detection, classification, and time-till-failure prediction. This will involve
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related discipline. Strong background/skills on machine learning, mathematics, probabilistic modelling and optimisation are preferred. To apply please contact the supervisor, Dr Mu - Tingting.Mu
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(agent-based modeling, differential equations) or machine learning tools. Good programming skills in one of the following programming languages: R, Python, MATLAB, or similar; Excellent English language
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deep learning to solve complex, high-impact problems. The ideal candidate will have a strong grasp of diverse machine learning techniques and a passion for experimenting with model architectures, feature
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processing, time series analysis or machine learning for the interpretation of structural data is desirable. Basic knowledge of numerical analysis and design of structures for special load cases (earthquakes
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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. He/she/they will learn and apply state-of-the-art molecular and cell biology technologies established in our team, ranging from in vivo disease models to multi-omics and single cell analysis
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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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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms