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Government of Canada | Government of Canada Ottawa and Gatineau offices, Ontario | Canada | about 1 month ago
through a research program in foundational mathematics and computer science, with concentrations in cryptology and machine learning research. Job Summary TIMC is seeking highly qualified researchers
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This research project, “Sensor-Free Machine Learning for Physiological Correction of Brain Functional Imaging: A Lifespan Pilot Study,” investigates how machine learning can be used to improve
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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow in Machine Learning for Computational Pathology, Medical Imaging, and Clinical Text Analysis Department Bashashati
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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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team in the AI in Medicine Lab (www.aimlab.ca ). This position is based in the School of Biomedical Engineering. The successful candidate will work in the AI in Medicine Lab, applying machine learning
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Liquid Argon Calorimeter system. Use novel high-level synthesis approaches developed internally (based on functional programming abstractions), to optimize the implementation of machine learning models and
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: Experienced in applying machine learning and data science tools (TensorFlow, PyTorch, pandas, NumPy, Scikit‑learn) to analyze and model complex atmospheric and environmental datasets. Proficient in multi
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style Attention to detail Clear oral and written communication skills Experience and Education BSc in a related field with relevant coursework 1-2 years additional experience Background in NLP, machine
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, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness, brings rich diversity to UBC as a workplace, and creates the necessary
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biostatistics and epidemiology Expertise in quasi-experimental, econometric methods and other advanced methods (e.g., longitudinal data analysis, trial emulation, interrupted time series, machine learning