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: Machine learning/deep learning model development for biomolecular data analyses and prediction Research Area: Data science and computational chemistry Required Skills: A Ph.D. in relevant field within
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Koziarski Lab - The Hospital for Sick Children | Central Toronto Roselawn, Ontario | Canada | 18 days ago
completed projects, preferably with publicly available repositories. Experience with deep learning frameworks such as PyTorch and JAX. Hands-on experience in developing and training deep learning models
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to develop deep learning models for analyzing whole-slide histopathology images, as well as natural language processing (NLP) methods for clinical records such as pathology reports and electronic health data
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The expected pay for this position is $70,000 per year + benefits. AI and Deep Learning for Genomics, Transcriptomics, and Bioinformatics Job Summary The School of Biomedical Engineering at the University
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projects across the following areas: Spatial and Single-Cell Proteomics in Childhood Cancer Cell-cell communication & cellular fitness in CAR-T & CAR-NK therapy Deep learning & LLMs in mass spectrometry data
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and managing scalable ETL (Extract, Transform, Load) pipelines to integrate multi-source, multimodal datasets Applying big-data analytic, including spatio-temporal modeling and deep learning techniques
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workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness
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the time of the appointment start date. Demonstrated expertise in current deep learning techniques (especially GNNs and/or RL) applied to biological data. Experience with spatial transcriptomics or single
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the appointment start date. Demonstrated expertise in current deep learning techniques (especially GNNs and/or RL) applied to biological data. Experience with spatial transcriptomics or single-cell omics datasets
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workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness