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Field
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publication trajectory. · Ability to work independently while contributing effectively to an interdisciplinary, collaborative research team. Preferred: · Experience with modern NLP and LLM techniques
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phenotyping, or healthcare data models. Chart review, diagnostic validation, or clinician-in-the-loop AI evaluation. Python tools for NLP/LLMs, such as Hugging Face, LangChain, LlamaIndex, spaCy, or similar
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machine learning, deep learning, natural language processing (NLP), computer vision, or large language models (LLMs) for biomedical or healthcare research. Experience integrating and analyzing multimodal
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for model-centric scholarly exploration This work sits at the intersection of AI/ NLP , scholarly knowledge extraction, software engineering, data infrastructure, and human-centered discovery systems
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science and engineering. Eligible subject areas include computational biology and/or biomedicine, multi-modality fusion and inference (visual and NLP data), computationally efficient ML
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Processing (NLP) and Computer Vision (CV) - strong record of publications - Excellent communication skills and ability to work in a fast-paced and innovative setting Preferred Qualifications (Project 1
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modeling, multimodal data fusion, interpretable machine learning, NLP, text mining, or automated extraction of materials data from the literature Experience with workflow automation, data infrastructure
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experience; and (c) a research publication record appropriate for the hiring rank. Knowledge of Arabic language is preferred, but not required. Previous work on Arabic NLP is preferred but not required. The
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. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP . Demonstrated working experience with healthcare data (e.g., EHR , clinical text
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(e.g., corpus construction, entity resolution, graph databases, text-mining/NLP pipelines). Experience developing research software that supports end users, including building web-based tools