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-assisted simulation framework by providing accurate high-fidelity numerical data for training and validation of surrogate models for multi-disciplinary design and optimization. · Participating in
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(sequence homology- or protein structure-based) Familiarity with UNIX/LINUX-based operating systems and shared compute infrastructure Proficiency in Python, R, or similar languages for data analysis
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to financial operations Develop systems for automated regulatory compliance checks, fraud detection, risk analysis, or smart contract orchestration Investigate the integration of agentic reasoning with financial
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analysis and determination of HIV clonality in HIV-infected cells to understand the mechanistic link between low-level protein expression and immune activation. Your Tasks: • Perform and analyze
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be
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). Maintenance of transgenic mouse colony and embryonic fetal analysis 3). Train students and post-docs in hematopoietic research techniques 4). Prepare figures. Make charts, tables, and graphs from numeric and
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: The doctoral candidate will perform computational analysis of a combination of multi-omics data from the gut microbiomes of patients with Alzheimer's disease or Parkinson's disease. The candidate will use state
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. Strong knowledge of quantitative and/or computational research methods, ideally in econometric analysis or optimization and simulation models. Preferable knowledge in Python and STATA. A collaborative team
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, and theoretical analysis using Leslie-Ericksen theory for liquid crystal flow as well as theory for viscoelasticity of polymeric liquids. Both in the practical and simulation/theory work you will be
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candidate in this project is element-specific analysis of the influence of electronic decoupling on the spin-state switching of SCO molecules by characterizing the magnetic and electronic properties of spin