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Field
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between CAR T-cells and the bone marrow microenvironment. This project will use spectral flow cytometry, in vitro and in vivo CAR T-cell assays and gene knockdown, overexpression, and CRISPR knockout
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where AI systems are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s
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tools that interpret large datasets from distributed sensor networks to predict and improve indoor environmental quality. Essential Functions Develop and refine AI- and machine learning–based models
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technologies and machine learning; and interact with collaborators and team members Prepare data reports for weekly meeting with PI and bi-monthly reports for weekly lab meeting Attend educational activities
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, analysis and evaluation, and writing reports which contain descriptive, analytical and evaluative content. The purpose of this role is to acquire the professional skills needed to pursue a career path of his
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where AI systems are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s
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approaches for binarized network models, identifying their strengths, limitations, and applicability within privacy-focused machine learning frameworks. Special attention will be given to evaluating
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: • Graph-based learning and community detection: Identify cohesive and antagonistic groups within signed networks. • Machine learning and network embeddings: Measure consensus, polarity, and opinion shifts
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analyzing numerical methods and/or machine learning methods for scientific computing. A strong computational background and experience with languages and frameworks such as Matlab, Python, NumPy/SciPy
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of causal reasoning tools, including causal inference, counterfactual analysis, causal discovery. Development of deep learning methods on computer vision. Job Requirements: Preferably PhD in Computer