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is using state of the art machine learning tools to extract interpretable latent dynamics. We seek a highly motivated PhD student to develop a predictive computational model using recurrent neural
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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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, as well you know how to work with a computer You work as a team player to solve problems analytically and work out solutions You are willing to learn something new every day and are willing to participate
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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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from the vibrant research community around machine learning of the SCADS.AI center ( https://scads.ai ) and the recently granted Excellence Cluster REC² – Responsible Electronics in the Climate Change
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essential. Good programming skills in at least one programming language (e.g., Python). Experience with machine learning, LLMs, or HCI/user study methodologies will be a plus. Strong interest in acquiring and
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community around machine learning of the SCADS.AI center (https://scads.ai ) and the recently granted Excellence Cluster REC² – Responsible Electronics in the Climate Change Era. We aim to attract the best
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presentation using common computer programs Ideally, strong communication skills in both German and English You are enthusiastic about learning new practical skills, approach unfamiliar topics with curiosity and
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multi-omics data integration and the project will provide opportunities to learn, develop, and apply machine learning and deep learning methods on genomics data. Requirements: excellent university and PhD
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). Knowledge of Docker and machine learning is considered a plus. Knowledge of standard bioinformatics tools for analyzing and interpreting Next Generation Sequencing data. Excellent oral and written