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of research and innovation! Be part of change Software development of the DSP toolbox for LiFi system Implementation of algorithms and software development using C++, Python or MATLAB Technical documentation
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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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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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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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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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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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mechanisms, optimisation algorithms and renewable energy systems WORK-LIFE BALANCE: Optimal conditions for balancing work and private life, as well as a family-friendly company policy. The option for flexible
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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
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dynamic systems, control engineering, and applied mathematics? Do you want to develop cutting-edge control algorithms for the security and resilience of cyber-physical systems? We welcome you to apply for a
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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