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protocols to characterize both cellular and vascular properties of the TME. The approach will be validated using a combination of in silico models, computer simulations, and in vitro experiments using tumor
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, biobanks, electronic health records); A sound understanding of Statistical and Machine Learning concepts, particularly in relation to genomics; Prior experience working with multiple data sources and
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survivorship programs in the world. We provide advanced, specialized training on the spectrum of late effects of cancer therapy. You will learn to recognize at-risk cancer survivors by performing risk-based
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. The objective of this postdoctoral project is to develop a unified, AI-compatible framework for non-neural behavior based on dynamical systems and learning. Behaviors will be modeled as low-dimensional dynamical
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trouble shooting. Support researchers training on best practices for data management and open science. Socialize research support services to scientists of diverse backgrounds. Perform outreach, teach
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Senior Bioinformatics Research Scientist - Northcott Lab in the Center of Excellence in Neuro-Oncolo
, epigenomics, single-cell, spatial 'omics, machine learning, and artificial intelligence. Experience in analyzing, interpreting, and visualizing human genomics datasets generated by high-throughput sequencing
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, military branch, conflict counselling). Experience in data entry and working in emergencies and fast pace, stressful environment. Some experience with computer systems, including Microsoft Office (Word
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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and services by utilizing the computerized scheduling system in an accurate, efficient manner. Maintains scheduling (clinic-specific) information and computer knowledge to ensure safe and effective
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for antibody repertoire sequencing and AI-driven research initiatives. Lead the integration of NGS into discovery workflows (in-vivo and in-vitro), emphasizing data generation for machine learning applications