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primary research areas: 1) statistical inference in high-dimensional and large-scale testing scenarios; 2) the development of novel model architectures for large-scale proteomics data; and 3) causal
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learning, parameter-efficient fine-tuning methods such as LoRA and adapter-based tuning, and retrieval-augmented generation (RAG) approaches. Familiarity with LLM architectures (e.g., GPT, BERT, T5
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and atomic-level assembly of cell walls, extracellular structures, and are constructing blueprints of how bacteria use these building blocks to engineer organized and dynamic architectures. We
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-scale multimodal datasets and collaborating with leading experts in spatial biology, AI, and cancer research. Responsibilities: Design and train state-of-the-art generative AI architectures (e.g
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and/or vision science Experience with Javascript, development of web apps and database architecture is a plus but not required Desire to work in a fast paced, collaborative team-science environment