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on two core but complementary areas: Computer vision and sensor data analysis, applied to tasks such as object detection in drone images (e.g., pest or disease detection), object tracking (e.g. leaves
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energy efficiency while keeping the grid reliable and secure. Our research method is engineering-oriented, prototype-driven, and highly interdisciplinary. Our typical research process includes
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Technology (CIT) and TUM School of Medicine and Health is offering a 2y-4y postdoctoral full-time position in medical machine learning. The Computational Pathology Lab (https://schuefflerlab.org
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-)Statistics, (Bio-)Informatics, Computer Science or related disciplines Strong background in modeling multi-modal data (images, tables, text, etc) Understanding of biases and causal inference Experience with
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Enthusiasm for an exciting new computing paradigm involving the development of innovative solutions Openness to communicate, cooperate and exchange ideas within a joint endeavor of multiple vibrant research