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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 2 months ago
: Large-Space Gaze Analysis via Multi-camera System for Social Interactions” to develop datasets and algorithms to capture and analyze eye gaze. About the project We are supporting the development of highly
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methods, Machine Learning algorithms, and prototypical Energy Management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a
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experimental settings. In addition to fieldwork, the PhD candidate will contribute to the development of novel inversion algorithms for EMI and GPR based on full-waveform inversion techniques. These methods aim
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. Because of the specific structure of the inference problems occurring in metabolic models, direct application of these MCMC algorithms is, however, not possible. In this project, you will bring MCMC methods
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following areas: Strong foundation in machine learning, optimization, and deep learning algorithms, including Transformer architectures. Hands-on experience or solid theoretical knowledge of LLMs/SLMs
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advanced mathematical algorithms and AI frameworks for modeling and optimizing power electronic components and systems. To support our team, we are looking for a working student (all genders) in the field
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efficient algorithms for large-scale stochastic systems Integrating data-driven methods for model estimation, learning, and validation Collaborating with interdisciplinary partners in healthcare and data
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to polymer-fluid coupling. Further areas of interest include numerical algorithms for high-dim. problems, classical (mainly finite elements, FEM) as well as alternative discr. meth. (e.g., Lattice Boltzmann
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process. By integrating simulation-informed priors into the reconstruction algorithm, we aim to significantly improve both accuracy and sensitivity. The position is hosted at the Fritz-Haber-Institut
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-critial vibrations from sensor data, enabling data-driven optimization of machining processes. You will also implemet ML algorithms for additional use cases, acceleration workflows and enhancing