8 machine-learning "https:" "https:" "https:" "https:" "https:" positions at ETH Zürich
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methods involving molecular surface display and deep sequencing to study force-dependent behavior in protein systems. These datasets will, in turn, be used to train machine learning models capable of
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-learning algorithms. A significant part of the position will also involve teaching and student supervision, particularly in the areas of embedded systems, FPGA design, electronics, and machine learning on
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across our hardware, muscles, and machine learning teams is expected and is what makes this work possible Profile You are extremely curious, highly motivated, greatly independent, and you want to make
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, Python, or Stata; experience with machine learning or advanced experimental methods is a plus) Familiarity with VR-related toolkits (e.g., Unity, Unreal, or eye tracking) is an advantage, especially for
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working largely independently on complex research topics and demonstrate strong motivation. We are particularly interested in applicants with a strong foundation in machine learning/deep learning and
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and machine-learning foundation models during the initial stage of the project to identify promising metastable compositions for experimental stabilization as thin films Presenting your results at
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Foundation. Profile A Master’s degree in civil engineering, geomatics, computer science, or a related field A strong background in machine learning, computer vision, or 3D point cloud processing, as
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feasibility assessment, randomization design, and retrieval of operational outcomes Working with multimodal embedding models and machine learning methods for content performance prediction Presenting