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Field
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, you will be an active member of our "Simulation and Data Lab for AI and Machine Learning in Remote Sensing", which aims to strengthen interdisciplinary research by bridging satellite remote sensing
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of reaching climate neutrality by 2050. By leveraging characterisation, multiscale modelling, machine learning, and numerical optimisation, the project seeks to craft a magnetic multiscale modelling suite
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, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with experiments, including
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for Astronomy in Germany. The StarForML group focuses on developing robust machine learning tools for the evaluation of star formation observations. We aim to gain new insights into how star formation progresses
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location Cottbus-Senftenberg and increase its visibility. Through innovative research and new teaching and learning formats, the BTU is shaping the future: with scientific findings and practice-relevant
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, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines Language requirements English C1 or equivalent Application deadline January, please see website for exact
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, Machine Learning Frameworks, ROS etc.) are needed and demonstrated expertise in at least one of neural networks or intelligent robotics is needed for these positions. Prior publication in a relevant AI
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to electron transport theory, machine learning, and molecular dynamics simulations, providing opportunities for students to acquire a broad range of skills. The close interaction with the experimental group
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the art tool built upon the scientific machine learning environment (“SciML”) in the modern “Julia” programing language for high-performance symbolic-numeric computation in scientific computing and
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on the data recorded in the team, you will develop and test machine learning algorithms for perovskite tandem solar cells' energy yield and degradation Data cleaning and preparation Assisting integration