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: Development of machine learning algorithms for the localisation of seismic sources (e.g., on 2D grid maps) Analysis and preprocessing of large DAS datasets Use of synthetic training data from seismic
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mathematics and its practical application. Our clients value our modeling expertise, algorithms, and software products. At Fraunhofer ITWM, we work on projects in high-tech and low-tech companies, in small and
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Isaac Sim, ensuring realistic physics for hybrid locomotion. You will develop and train RL algorithms for hybrid locomotion tasks, including transitioning between locomotion modes and balancing on uneven
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and AI algorithms Solid programming skills in Python and familiarity with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) Experience working with geospatial data (e.g., geopandas
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robotics Goal-driven agentic AI Autonomous medical imaging Design of AI-enhanced medical devices Machine learning models and algorithms for medical signal processing Embedded AI Privacy-aware AI Foundations
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technicians Image analysis using AI-segmentation algorithms Histological correlation of PET results and immunohistochemistry for tissues transduced by the vector Experimental design, realization, and analysis
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of research and innovation! Be part of change Design and development of IP cores for low-latency, high-throughput digital signal processing Simulation and verification of the implemented algorithms with test
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Your Job: Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep
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to explore and develop AI algorithms, frameworks, and hardware architectures for efficient edge deployment in vehicles, with a focus on neuromorphic computing. You will be part of the scientific TUM HN Team
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, internationally oriented center for cutting edge research on neurodegenerative diseases. We are seeking a highly motivated and skilled Postdoctoral Researcher to develop new algorithms for multi-omics data