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are required to have: A completed PhD degree. Experience in machine-learning methods. Some skill in at least one of these topics: Large data sets analysis Statistics and uncertainty analysis (probabilistic
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, serving as a basis for statistical evaluations of stone formats, brick bonds, and the like. Where applicable, you also perform geometrical analysis of vault geometries and traces of formwork. You interact
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signal processing (GPU based) Proficiency in data analysis using Python, Matlab, or similar Self-motivating, independent-minded scientific researcher, effective collaborator Excellent written and oral
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long as it includes some form of hands-on digital practice as a central component, but an engagement with critical AI studies, design approaches, game studies, code studies, and/or ethnographic
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-evaluation and signal-analysis pipeline. The work will be performed in close collaboration with electrical engineers and neurobiologists. A broad spectrum of state-of-the art engineering and testing equipment
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team members Perform accelerated optical degradation tests of transparent conductive materials Apply machine learning techniques for data analysis and time-series forecasting Collaborate in a
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, vaporization, solidification), vapor plume formation dynamics, and residual stress analysis. The objective will be to investigate these phenomena using simulation tools and via sophisticated experiments. Besides
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(GPU based) Proficiency in data analysis using Python, Matlab, or similar Self-motivating, independent-minded scientific researcher, effective collaborator Excellent written and oral communication skills
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engineering, health sciences and technology, or a related field Strong technical and signal analysis skills with programming experience (e.g., Matlab, Python or a similar language) Experience with (f)MRI, EEG
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ancient DNA samples, and experience with high-performance computing environments. Description - Conduct whole-genome sequencing and data analysis to assess the genetic diversity of wild bee populations in