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Field
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Senior Scientist / Group Leader on Bioinformatics / Computational Biology on RNA Regulation in Disea
, and validate computational findings Apply machine learning and statistical modeling techniques to identify patterns and predict functional impacts of RNA modifications Contribute to publications
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of embedded machine learning, neuromorphic hardware and deep learning accelerators. Want to get more information? Click here. What you will do Responsible for RTL design (VHDL, Verilog) of digital blocks and
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W3 or W2 with tenure track to W3 professorship in General Geophysics / W2 professorship in Geographi
measurement methods, experimental and numerical simulations, and machine learning/big data. Our future colleague is expected to significantly contribute to establishing the new interdisciplinary focus on Earth
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, computer vision, pointcloud-processing or machine learning is a plus Analytical mindset and experience in algorithm development Enthusiasm for mobile robotics Fluent in English or German What you can expect
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., experience sampling, virtual/mixed/extended reality, machine learning, artificial intelligence) are desirable. The professorship, integrated into the Chair of Personality and Educational Psychology, is
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(Methods for Active Informed Machine Learning). This project is a close collaboration with the Hasso Plattner Institute. We are developing and improving machine learning methods by integrating domain
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methods in AI and machine learning, contributing directly to state-of-the-art research with high industrial relevance. Your Qualifications A strong background and Master's degree in Computer Science, AI
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that exhibit emergent turbulent behaviors, and (2) disordered optical media that process information through complex light scattering patterns. Using advanced imaging, machine learning techniques, and real-time
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approaches and will integrate novel hardware (including electrode arrays, microdevices, analytical systems) into automated robotic pipelines You will also apply machine learning-based analyses to imaging and
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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