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Field
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
related field # Proficiency in programming languages (Python, C/C++, Julia) # Background in machine learning methods # Experience in developing, training, and tuning machine learning models # Prior exposure
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, aerodynamic, and aeroelastic testing. Develop and apply advanced numerical models (e.g., FEA, CFD) to simulate wind effects on structures. Analyze and interpret experimental and numerical data, and disseminate
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of reactor systems. Knowledge of heat transfer and fluid dynamics for nuclear system applications and reactor safety analyses. Knowledge of computation methods and numerical solvers for engineering
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critical role in advancing computational materials science by developing and applying first-principles and machine learning methods, with a focus on interatomic potential development and large-scale
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that integrate simulation, machine learning, and data analysis. Numerical optimization methods (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
. The Department Biostatistics at UNC Chapel Hill has numerous exciting research programs of relevance to health science, including focus areas in genomics and big data. This position is funded by a training grant
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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of the project Bacteria are constantly predated by viruses, bacteriophages. To resist predation, bacteria employ numerous antiphage defence systems, with the most famous being CRISPR-Cas. In our lab we discover
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means of CMOS-based high density microelectrode arrays (HD-MEAs). The overall goal of this project is to combine advanced optical methods for light-stimulus shaping with high-spatiotemporal-resolution
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, genomic datasets, machine learning, and experimental methods to investigate how the tumor microenvironment and gene regulatory factors control tumor metastasis cascade. By advancing our understanding