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Field
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statistical and machine learning techniques for dynamic energy system modelling Develop advanced optimization algorithms for building energy management and control (e.g., MPC, RL) Develop and evaluate digital
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methods (LBM). For fluid simulations, we utilize the high-performance LBM framework waLBerla, predominantly written in C++, but increasingly adapted for GPU computations through automatic code generation
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. The role involves setting up experimental models using Particle Image Velocimetry (PIV) and performing Computational Fluid Dynamics (CFD) simulations to analyze fluid flow and deposition patterns. The data
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(assessment of membrane processes and water quality analysis) are required. Experience in computational fluid dynamics modelling of water treatment and desalination processes is an advantage. Willingness
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emphasis on hydrocarbon reservoir systems. We are especially interested in candidates with research interests in geomechanics, tectonics, sedimentary basin dynamics, and hydrocarbon reservoir systems
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Towson University - Fisher College of Science and Mathematics | Suburb Maryland Fac, Maryland | United States | about 1 month ago
, Maryland 21252, United States of America [map ] Subject Area: Computational Fluid Dynaimcs Appl Deadline: none (posted 2025/02/27) Position Description: Apply Position Description Postdoctoral Lecturer
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approaches and research themes here: www.scilifelab.se/researchers/lisandro-milocco/ This project leverages the rise of data-driven dynamic modeling—from fluid dynamics to ecosystem studies—to uncover
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A knowledge of fluid dynamics is recommended. Personal characteristics Working as a Postdoctoral fellow requires that you The successful applicant is a highly competent, motivated and ambitious
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Area of research: Scientific / postdoctoral posts Job description: GFZ is Germany's national centre for solid Earth research. We advance the understanding of dynamic processes to address global
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, along with excellent oral and written communication skills in English and strong publication preparation skills. About the Department The St. Anthony Falls Laboratory (SAFL) is an interdisciplinary fluid