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Field
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Responsibilities: Build and analyze dynamical system models (multiscale, QSP, PBPK, PK-PD). Apply numerical methods, optimization, and parameter estimation to calibrate models to experimental/clinical data. Perform
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Engineering, or related. Strong experience in ODE/PDE modeling and simulation (MATLAB, Python, or R). Experience withnumerical methods, optimization, parameter estimation, and sensitivity and uncertainty
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methods to ultimately let dermatologists continually update multi-modal machine learning models. Our research objectives are to 1) develop novel model editing methods for multi-modal models, with a focus on
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artificial intelligence with a preferred focus on computer vision and medical image analysis. Preferred Qualifications: PhD in Medical Physics, Bioengineering, Biomedical Engineering, Physics, Computer Science
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artificial intelligence with a preferred focus on computer vision and medical image analysis. Preferred Qualifications: PhD in Medical Physics, Bioengineering, Biomedical Engineering, Physics, Computer Science
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Posting Description POSTDOCTORAL ASSOCIATE, Mechanical Engineering, to perform cutting-edge research on projects centered around machine learning models for Computer Aided Design and Engineering
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data analysis with experience in Bioinformatics, proteomics, RNA sequencing and RNA sequencing data analysis, and multiomic integration and analysis. Development of computational analysis methods is
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, or reading groups), present their work at research methods and field-specific seminars, assist with DDSS projects on occasion, and offer consultation to faculty, graduate students, and postdoctoral researchers
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to translate rigorous methods into practical tools. QUALIFICATIONS: Ph.D. in Applied Mathematics, Electrical & Computer Engineering, Computer Science, Industrial Engineering, or a closely related field by start
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for method validation and quality control.10% Assist with field sampling campaigns at commercial pecan orchards and vineyards across Texas. 5% Analyze data using appropriate statistical approaches. 5