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Python, MATLAB, or C++. Experience with machine learning techniques, CAD, computational modeling, 3D printing, motion capture, and/or material testing. Proficiency in programming languages commonly used in
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modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control
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) Development of an Augmented Smart Classroom for Personalized Learning (SmartClass) serving as a test-bed for the collection and analysis of students and professors data, leveraging on data analytics and machine
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growth for Portland, Maine, and northern New England. We are nurturing an environment for high-impact research and innovation in computer and data science, digital engineering, the advanced life sciences
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reactions. We welcome applicants from diverse backgrounds, including computational chemistry, bioinformatics, systems biology, and machine learning. The project offers a unique opportunity to collaborate
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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 1 month ago
or Postdoctoral position (m/f/d) - Interpretable Machine Learning for Catalytic Reaction Network Discovery. A full-time PhD or Postdoctoral position is available in a collaborative Max Planck research
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of Biostatistics at University of Florida and Dr. Hongkai Ji (remote) in the Department of Biostatistics at John Hopkins University. This position, available immediately, focuses on developing statistical, machine
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to substantive political science questions. You have strong skills in automated text analysis and natural language processing (e.g., machine learning including neural networks, relation and entity extraction
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in the area of scientific computing and Computational Fluid Dynamics. Prior Experience in turbulence modelling, machine learning or the Lattice Boltzmann method is an advantage. Operational skills
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of Science at UvA. What are you going to do? The aim of the project is to use advanced Machine Learning techniques to predict the anharmonic vibrational spectra of large Polycyclic Aromatic Hydrocarbon (PAH