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Project title Multi-Modal Large Language Model for Medical Image Analysis Research period 2 years Abstract The proposed research project aims to develop a novel multi-modal large language model (MLLM
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. Particular emphasis to be placed on developing and applying emergent system identification and modal decomposition techniques as well as statistical analysis of large datasets. Candidate will disseminate
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://pritykinlab.princeton.edu) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi-modal single-cell, spatial and genome editing
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technologies. The Pritykin lab (http://pritykinlab.princeton.edu ) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi
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exceptional postdoctoral research fellows interested in developing deep learning and computational methods for pathology image analysis, multimodal data integration, and other medical modalities (e.g
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(e.g. phonetic, typological, geo-historical, topographical, and sociolinguistic) variably promote or inhibit prosodic convergence between languages in contact, and map findings on a digital multi-modal
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, visible, and ultraviolet; acoustic; radio spectrum; magnetic field strength; charged particle count; and environmental sensors. The project aims to conduct an exhaustive, long-term, multi-modal aerial
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processes or laws Elicitation of requirements from natural language Applications of declarative specifications (e.g. temporal, modal logics, declarative process models) in the analysis of systems Model-driven
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research tasks investigating navigation and integrity assessment of several aircraft-related sensor modalities. The candidate will be mentored by Dr. Jay Wilhelm (Associate Professor of Mechanical
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the sensory coding of orofacial thermal, Chem esthetic, and taste sensations, and cross-modal and multisensory interactions between somatosensory and gustatory signals in the periphery and brain. The Lab’s work