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high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms. No prior biomedical training is required—just strong quantitative
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computational approaches to uncover novel biomarkers and therapeutic strategies for CNS disorders. Key Responsibilities: Develop and implement algorithms for multimodal image fusion, combining data from MRI, PET
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Biochar Production, Syngas Generation, Green Hydrogen Separation, Net-Zero Heat Distribution Systems, Advanced Electrochemistry, and Integrated Spectroscopy. The role integrates materials design
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algorithms, NLP models, and LLMs to analyze complex data. Designs and implements novel data science methodologies for predictive modeling, causal inference, and probabilistic analysis in clinical and
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records and proper care of reagents and animal models. Engage in open and timely communication with the mentor regarding the possession or distribution of materials, reagents, or records belonging
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. The most recent methodological research of the group includes algorithms for cell type deconvolution, high-resolution purification, and integration of single cell multi-omics data. This postdoctoral fellow