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development utilizing the ancestral recombination graph (e.g. Fan et al. AJHG 2022 , Fan et al. bioRxiv 2023 , or Link et al. AJHG 2023 ). Postdoc position is for 1-3 yrs depending on the career goals
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, though not required. Knowledge of bioinformatics is highly advantageous, but not mandatory. Certifications/Licenses N/A Required Knowledge, Skills, and Abilities Proficiency in graphing software and
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, graph theory, and optimization techniques Furthermore, our activities are experimentally driven and supported by the COMMLab , the 6GSPACE Lab , the CSATLab , the HybridNetLab , the QCILab , our SW
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computer science using data-driven techniques (graph theory, ICA, machine learning), in other imaging modalities (DTI; MEG), and in multimodal integration will be relevant. Experience with AFNI/SUMA, SPM, FSL
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existing data and generate manuscript proposals. Perform data analysis and draft comprehensive APA-style results sections, including tables, graphs, and figures. Recruit and educate potential patients
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areas: Generative AI Agentic AI Graph Representation Learning and Modeling Foundation Models Large Language Models Multimodal Learning Basic Qualifications A Ph.D. or equivalent degree in Machine Learning
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the results (e.g. worksheet, graphs, tables, etc.) and assists in developing appropriate computer programs. o Analysis of data obtained as a result of experiments performed, and preparation of laboratory
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disease. Keep accurate and timely records of experimental procedures Perform data processing, analysis, charting/graphing, and presentation as directed by Principal Investigator (PI) Perform literature
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., knowledge graphs) and patient electronic health records (EHRs) Familiarity with biomedical terminology Interest in equity, bias, and representation, in both evaluating the skews of datasets and the
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to a problem in some domain such as biology, social science, economics, etc. Alternatively, experience working with high-throughput omics, text, or graph-like datasets. Proficiency in scientific