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Field
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manufacturing and laser material processing. We are currently developing machine learning-based approaches to make the laser powder bed fusion (LPBF) process more efficient and improve its quality. To this end, a
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developing a machine learning (ML) algorithm for the automated analysis of the above-mentioned mass spectra. Desirable: - knowledge in the field of Planetary Sciences - very good written and spoken English (C1
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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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technology. What you will do As part of an internship, we offer you the opportunity to deepen your interest and knowledge in the field of machining. You will learn how to use CNC machine tools and manufacture
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and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) A high motivation and the ability to work independently with a strong team
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established that reliably identifies the connected components in the diagrams. You will learn about novel AI models and exchange ideas with experts from the building sector. The "Image Processing and Machine
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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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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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and machine learning to establish a modeling framework that uses omic data for providing effective degradation rates of biomolecules and predictions of their impact on soil organic matter turnover
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of interest include, but are not limited to: AI methods that meet the complexity of living systems, high-dimensional machine learning for biology, statistical machine learning, AI‑driven laboratory automation