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Field
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datasets (e.g., images, surveys, statistical and sensor data). Familiarity with geostatistical, GDAL, Python, PostGIS/PostgresSQL, Machine Learning, AI, Internet of Things and Remote Sensing datasets is
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scientists, biomedical informaticians, clinicians, and public health researchers to develop deployable, trustworthy methods that improve patient outcomes and health system operations. Key responsibilities
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Ability to do original and outstanding research in computational biology, and expertise in computational methods, data analysis, software and algorithm development, modeling machine learning, and scientific
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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow in Machine Learning for Genomics, Transcriptomics, and Bioinformatics Department Bashashati Laboratory | School
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
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and algorithm development, modeling machine learning, and scientific simulation Ability to work well in an interdisciplinary environment, and to collaborate with experimentalists Strong oral and written
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large scale flow network simulations, machine learning, and methods from topological data analysis, to a broad set of problems. Examples include modeling vertebrate and invertebrate circulatory systems
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biology, protein engineering, biochemistry. Optical engineering, fluorescence microscopy, image analysis: Development of microscopes and data analysis pipelines used to acquire and quantify high-throughput
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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Requirements: PhD in geophysics, geology, geomatics, geodesy, geomatics, electrical engineering, computer science, natural hazards, or a related field Demonstrated skills in remote sensing, image processing