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of production and manufacturing technologies, as well as the necessary machine and control technology for the manufacture of innovative products. What you will do Support in the creation, conception, coordination
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contribution of genetic and non-genetic driving forces for the cells’ evolution and glioma development. Using multi-omics data integration and machine learning, we will investigate cellular behaviors and gene
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or CNC machines You have already worked with Python or other data analysis software, or are motivated to acquire the relevant skills What you can expect 👥 Team spirit: Creative and interdisciplinary
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of error mitigation and error correction primitives. Thereby, the applications of quantum computing that this group is working on is diverse, ranging from various machine learning methods over optimization
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neural simulators (NEST, Brian, etc.) and/or machine learning frameworks (PyTorch, Tensorflow, etc.) is a plus Experience with spiking neural networks and/or neuromorphic computing is a plus Please feel
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, check your computer’s network connection. If your computer or network is protected by a firewall or proxy, make sure that Firefox is permitted to access the web. You can continue with your default DNS
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Curriculum and other under and post graduate degree programmes that involve the Faculty of Medicine. Further, they will be expected to teach in areas outside their specialisation. The successful candidate will
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in high-performance computing, materials chemistry, theoretical chemistry, molecular dynamics, data science, and machine learning are beneficial. What we offer: We offer a position with a competitive
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novel machine learning-guided approaches. The position is located at TUM Campus Heilbronn. Your qualifications Strong background in computer science, AI, or related areas or similar fields. Solid
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electrochemical impedance spectroscopy (EIS) directly during the disassembly process to classify the cells for their reusability. A pre-trained machine learning model for assessing cell condition based on EIS data