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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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may also involve complementary atmospheric modelling or alternative inference approaches, including machine learning and AI. Possible focus points for personal research performed by the successful
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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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, 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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photocathode operation, and investigate machine-learning/AI-based optimization to improve performance Study photocathode and laser performance, including the lifetime of semiconductor photocathodes at
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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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feasibility assessment, randomization design, and retrieval of operational outcomes Working with multimodal embedding models and machine learning methods for content performance prediction Presenting
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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