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Field
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open-source code (Python) that is high-performing and scalable to comprehensively quantify uncertainties using probability theory. This is where you will contribute: in the application of the developed
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technology experts work together to build a framework on open-source code (Python) that is high-performing and scalable to comprehensively quantify uncertainties using probability theory. This is where you
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of complexity theory, systems thinking, and simulation modeling. • Stakeholder management, marketing strategies, and customer satisfaction modeling. • Both basic and applied research, including contract research
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systems theory Excellent analytical and problem-solving skills Desirable criteria Advanced programming and data analysis skills Computational neuroscience background Behavioral data analysis skills Strong
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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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studies and interactive AI systems. This position will be funded based on an initial 2-year contract + 2 years extension. The key idea is to apply theories, models, and methods from psychology to improve
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of engineering technical procedures, principles, theories and concepts in the field. General knowledge of other related disciplines. Demonstrates leadership in one or more areas of team, task or project lead
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recordings Familiarity with neuroanatomy and neurophysiology Knowledge of dynamical systems theory Excellent analytical and problem-solving skills Desirable criteria Advanced programming and data analysis
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large-scale MD simulations, ideally with LAMMPS, demonstrated via corresponding roles in publications Experience with density functional theory (DFT) calculations, ideally with VASP, demonstrated via
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focus more on model development, robustness, and long-term reliability. What you can expect Modelling. Apply probability theory, statistical analysis, and machine learning techniques to build robust