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areas: + Quantum computing and quantum algorithms + Solid-state physics, computational materials science, or quantum chemistry + Battery materials modeling Excellent programming skills (e.g., Python) and
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/@signalprocessingunioldenbu1018 ). Your tasks conduct research on the use of foundation models for real-time sound processing algorithms suitable for hearing aids (e.g., acoustic scene analysis, noise reduction, speaker separation
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applying machine learning algorithms for solving problems in protein engineering. Written and oral command of English is essential. Research environment The Max Planck Institute of Biochemistry, Martinsried
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on bipolar plates. The internship focuses on the algorithmic evaluation of camera images. Your tasks will include researching and prototypically implementing the evaluation algorithms, as well as designing
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optimisation of analytical and numerical models for metasurfaces Documentation of the algorithms developed and results What you contribute You are enrolled in a Master's programme, e.g., computer
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iteratively improve both the setup and the algorithms. What you contribute Very good C++ programming skills Strong knowledge in mathematics and linear algebra Proficient English skills Precise, responsible
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: Design hierarchical models that explicitly capture misspecifications in metabolic models Develop differentiable and scalable inference algorithms using automatic differentiation Implement HPC-tailored
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the next era of the quantum revolution. Together, we research, develop, and design innovative quantum algorithms that push the boundaries of what is possible- especially in the natural sciences such as
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excited to make a real impact with their thesis work in the field of robotics; Strong programming skills in C++ and/or Python, as well as experience in implementing robot learning algorithms; A strong
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“Stability and Solvability in Deep Learning”. This project focuses on mathematically analyzing machine learning algorithms with a particular focus on questions of stability, computability, and robustness