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Field
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Machine Learning Cluster of Excellence TERRA CIN LEAD Graduate School & Research Network Collaborative Research Centers Transregional Collaborative Research Centers (CRC-TRRs) DFG Research Units Research
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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with
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qualification you must hold a PhD degree (or equivalent). Specifically, a PhD in manufacturing engineering (or equivalent) with documented experience in the following areas: Precision machining Machining system
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knowledge of wireless communications, and signal processing. You have at least intermediary knowledge of machine learning algorithms, including federated learning, split learning, and graph neural networks
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) Developing machine-learning based exoskeleton controllers to work across tasks 2) Designing and validating new robotic lower-limb prostheses 3) Exploring other high-risk high-reward research areas related
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engineering, data science, or related fields Strong programming skills (especially in Python), and experience with simulation, modelling, data analysis, machine learning, and hardware control Solid
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale
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quantitative methods, data mining, machine learning, and AI applications. Macroeconomic research on monetary policy, financial stability, banking sector dynamics, housing affordability, labor markets (e.g
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural