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interest in data analysis, modelling, statistics, and machine learning. Experience in spatial data analysis (GIS), scientific programming (Python, R, or equivalent), or artificial intelligence will be
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interactions between individual cells. Where to apply Website https://www.academictransfer.com/en/jobs/362788/post-doc-learning-interactions-… Requirements Specific Requirements PhD Degree in Physics Preferably
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benchmark chemometric and physics-informed machine learning models to monitor, forecast, and ultimately control critical process parameters, implanting these models in advanced control frameworks to optimize
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PhD Scholarship in Development of Cement-Free Living Building Materials for Sustainable Construction
commitment to research excellence Possess the ability to work independently, take initiative, and drive research activities forward Show adaptability and a willingness to learn new concepts, techniques, and
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designed for the 13 Research Fellows within the BIO-MICRO consortium. The BIO-MICRO training programme offers a blended learning approach that combines monthly interactive live online sessions (60–90 minutes
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processes. • Apply machine learning techniques and advanced statistical analysis to extract knowledge from complex datasets. • Participate in the evaluation and optimisation of high-performance scientific
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important that you are able to: demonstrate strong motivation, curiosity, and a learning-oriented mindset work independently, take initiative, and maintain good structure and discipline in their work
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
(or surrogate models) are approximations of classical numerical solvers with a very low computational cost. They form the core of a digital twin. Using machine learning techniques to build these meta-models
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University of Girona (UdG) - Institute of Computational Chemistry and Catalysis (IQCC) | Spain | 3 months ago
learning, and enzyme engineering concepts, the project seeks to establish new design principles for creating biocatalysts capable of performing challenging and non-natural chemical transformations
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to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches