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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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join the group to develop AI and machine learning based software to assist clinical workflow and pre-clinical studies. Required Qualifications: Ph.D. in a physical science or engineering field Strong
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software (e.g., MPlus, R, or Stata). Applicants must have graduate training in psychology, neuroscience, medicine, or another advanced degree in a related discipline (Ph.D., Psy.D., M.D., or equivalent); and
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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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prototype plug-in so that LCLS/qRIXS analysis workflows can call the Legion/KDRSolvers-backed RIXS solver, establishing the “not an island” software pathway requested by the directorate. Explore
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, ministries of health, and local enumerator teams to manage survey logistics. Oversee enumerator training, pre-testing, and quality assurance protocols. Code survey into appropriate software (e.g., SurveyCTO
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software (e.g., WaterTAP) and programming languages (e.g., Python) is highly desirable. Familiarity with the principles of electrified mineral extraction and purification processes is advantageous but not
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econometrics) or relevant research experience. • Strong coding skills in R, Stata, or other statistical software package. • Good communication skills in English. Required Application Materials: CV (no cover
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neuropsychological assessment with brain imaging data (e.g., functional MRI, structural MRI, EEG) Proficiency with neuroimaging analysis software or electrophysiological data processing Experience conducting clinical