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/ sonar, communications over dynamic channels, orthogonal time frequency space (OTFS) modulation, shared-spectrum / RF convergence, machine and deep learning (e.g. model-aided, convergence analyses
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of life science research experience. · Experience working with mouse models of disease. · Technical aptitude for learning and executing complex experimental techniques for molecular biology, cell-based
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biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM data and spatial-omics data collected from state-of-the-art
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: Durham, North Carolina 27708, United States of America [map ] Subject Areas: Computer Science / Augmented Reality , Programming Languages Electrical and Computer Engineering / Machine Learning Appl
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with causal inference methods or machine learning approaches Demonstrated experience in scientific writing and publication Ideal for candidates who: Have recently completed (or are near completion of) a
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computing, machine learning for hardware design, integrated circuit design, or hardware–software co-design. Experience with semiconductor design tools, circuit/system modeling, or large-scale hardware design projects
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digital health and multi-agentic coordination. This role requires the development of self-learning artificial intelligence models tailored for dynamic healthcare environments. The candidate designs and
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. Candidates with background knowledge and hands-on experience in mouse models, proteomics, 3Dorganoids,primary cells purification and culture skills are particularly welcome. Minimum Requirements: Ph.D
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direction and supervision. •Statistical analysis and database management. •Learn and execute on Systems dynamics modeling and/or microsimulation Mixed-methods community engagement methodological development
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for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell