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machine learning-based data analysis, and supervising students involved in electrochemical experimentation * submission of manuscripts from the project results and seeking additional funding to expand
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the 1st cycle and/or 2nd cycle studies, in particular in the subject areas connected with international and national security, international military relations and defence policy, ability to conduct classes
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(Source Term Estimation for European-Scale), enabling the identification of source terms on a continental scale. The innovative aspects include the use of a new forward atmospheric dispersion model, machine
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to, the development of new methods for multiomics or image analysis, approaches using artificial intelligence (AI) and machine learning (ML), or advanced numerical simulations. Your expertise will enrich IIMCB’s
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, including those based on hardware and software platforms (e.g., Arduino). • Basic knowledge of machine learning and familiarity with popular tools (e.g., Scikit Learn, Tensor Flow, Keras). • Proven record
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and organizational work; Have proficient computer skills, including knowledge of applications for conducting distance learning classes; REQUIRED DOCUMENTS: 1. Application form to the Rector
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proficient computer skills, including knowledge of applications for conducting distance learning classes; REQUIRED DOCUMENTS: 1. Application form to the Rector of The University College of Applied Sciences in
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bio materials and porous materials PhD student candidate 2 with background in computer science, AI, machine learning or related fields with the experience in CFD, ANSYS, COMSOL The successful candidates
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on knowledge retrieval and interactive, interpretable machine learning to increase the efficiency of the drug discovery process. The developed tool will be able to analyze images of cellular events, such as
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, machine learning, Experience in human electrophysiological research is a plus, experience in intracranial human research large plus, Knowledge of cognitive system is a plus, knowledge of neuronal basis