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systems spanning from novel physical layer waveforms designs (e.g., MIMO, OTFS) to artificial intelligence-enabled networking protocols; from reflecting intelligent surfaces (RIS)/integrated sensing and
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candidate will join Dr. Siddharth Saksena’s research group, which focuses on advancing hydrologic modeling, flood forecasting, and hydroinformatics through the integration of artificial intelligence, physics
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several areas of ongoing research in the laboratory, including the isolation and genomic analysis of large DNA viruses, cultivation of diverse hosts, analysis of evolutionary relationships and genomic
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the following directions: 1) Developing computational methods for integrating multi-modal data, such as scRNA-seq, scATAC-seq, spatial transcriptomics, ChIP-seq, and CRISPR screening. 2) Investigating context
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supervision of Prof. Yingda Cheng on computational methods and modeling for kinetic equations. The research conducted will involve development of numerical methods, development and analysis of reduced order
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concurrent duties - Excellent communication skills - Strong track record of publishing in peer-reviewed journals Preferred Qualifications - Proficiency with data analysis and visualization in R - Experience
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treatment, analysis and remediation technologies. This position will actively contribute to federally funded and industry-supported projects. The initial appointment is for one year with opportunities
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. The Postdoctoral Associate is expected to lead research projects from experimental design, data analysis, to presentations in lab meetings, conferences, and seminars, as well as prepare manuscripts for peer-reviewed
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related technical field, where prior research experiences in the analysis of aerodynamic noise, vortex sound, and/or wave scattering are highly valued. The postdoctoral scholar will benefit from
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optogenetics, light-controlled gene expression systems, and photoreceptors. Familiarity with metabolic modeling (e.g., COBRA methods), genome-scale modeling, or flux balance analysis. Experience with Raman