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communication and collaborative skills. Experience with SLAM, sensor fusion, LiDAR/depth camera data processing. Familiarity with deep learning for obstacle avoidance (e.g., map-less navigation). Background in
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or prior experience with multilingual or low-resource NLP Programming skills in Python and proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow) and their application in high-performance
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weekly working time of 40 hours per week. The position can be filled on a part-time basis. Background: Addressing climate change and biodiversity loss requires a deep understanding of global land-use
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in adaptive immune systems (e.g., co-evolution of bacteria and phages, as well as T and B cells with pathogens). • Physics-informed machine learning of biophysical systems (e.g., developing optimal
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, deep learning, and data analytics will be beneficial. 4. Experience in Dynamo programming would be preferred. Willing to learn and responsible. 5. The appointed candidates will support the planning
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learning, or deep learning models Salary Information Commensurate with experience Review Date March 10, 2025 Additional Information The successful candidate will be required to have a criminal conviction
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skills. Excellent programming skills in Python and Julia with experience with deep learning frameworks (e.g., PyTorch, TensorFlow). Experience building complex software systems, preferably with industry
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in the University of Minnesota. The research will focus on applying, developing and implementing novel statistical methods for causal inference, integrative data analysis or/and machine/deep learning
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functional data analysis, tensor regression, high-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated
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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past