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tune the model parameters and validate the models under different operational conditions. In these phase optimization methods and machine learning techniques can be used to make the process more
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or equivalent Skills/Qualifications Doctorate (PhD) in science or engineering, preferred in Physics, Mathematics, Electronics or Computer Sciences; experience in deep learning modelling or research (theoretical
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computer science, including data analysis, machine learning, big data and intelligent computing. In addition, application research in the above mentioned methods and techniques in relation to machine learning is
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, including data analysis, machine learning, intelligent computing. In addition, application research in the above mentioned methods and techniques in relation to machine learning is expected. The teaching
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the performance and supervision of geological engineering and hydrogeological drilling works. Good knowledge of the English language (minimum B2 certificate and the ability to teach in English). Good knowledge
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The person employed in this position should be ready to conduct research in the area of machine learning, especially in the field of deep learning (preferably image analysis, but audio, NLP or other
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The research topics will be related to the implementation of the project: Multispectral fluorescence supported by machine learning for the analysis of cell cocultures. The scientific goal of the project is to
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activities in accordance with the university's regulations working in research and teaching teams in the area of machine learning and signal analysis developing signal processing methods developing machine
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scientific research in the field of image processing and machine learning; 2) publishing research results and participation in scientific conferences; 3) conducting didactic classes and preparing teaching
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reality, computer simulation, 14. DESCRIPTION (field, expectations, comments): The candidate for this position should be prepared to teach in the area of applied computer science, especially in the subjects