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Field
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family of microcontrollers and embedded systems design; modelling and simulation using MATLAB/Simulink; signal theory and digital signal processing, including time-domain, statistical, and spectral
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monitoring and quantitative analysis of changes in fluorescence signals. The results obtained may directly contribute to the development of more effective and safer targeted therapeutic strategies. A candidate
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monitoring and quantitative analysis of changes in fluorescence signals. The results obtained may directly contribute to the development of more effective and safer targeted therapeutic strategies. A candidate
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State University of Applied Sciences Memorial of Prof. Stanislaw Tarnowski in Tarnobrzeg | Poland | 4 days ago
conducted by the State University of Applied Sciences Memorial of prof. Stanisław Tarnowski in Tarnobrzeg. Documents listed in point II. 1, 2, 3, 6 (in the case of a statement) 7, 8, 9, 10 should be signed
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against bacterial pathogens at the single-cell level. Using Listeria monocytogenes (Lm) as a model, we investigate how variability in host signalling and bacterial behaviour determines infection outcomes
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activity, including authorship or co-authorship of the publications referred to in point 2. 4. Experience in scientific research, including the implementation of multicenter projects. 5. Experience in
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processor programming or the use of DSP units in microcontrollers, Raspberry PI platform programming, programming in the Matlab/Simulink environment, signal theory and digital signal processing, including
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selected in an open competition, according to the procedure complying with the rules of the European Charter for Researchers and the Code of Conduct for the Recruitment of Researchers as a part of HR
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InLife Institute of Animal Reproduction and Food Research, Polish Academy of Sciences | Poland | 10 days ago
by 12 October 2026 to: [email protected] , with "Postdoctoral Researcher in ZRiRR Team" in the subject line. Selection process After initial analysis of the submitted applications, we will
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optimization of machine learning methods for processing Quantum OCT (Q-OCT) signals. Design, training, and testing of neural network architectures for artefact and dispersion removal in Fd-Q-OCT and SS-Q-OCT