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systems that fuse multi-modal remote sensing, soil and phenology data to enable crop classification and precise, customized fertilizer recommendations. Selection criteria (short) Required PhD (awarded
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techniques and methodologies for cell analysis Analyze interpret complex biological data Publish scientific results in peer-reviewed journals Present research results at national scientific meetings Have a
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attitude and ability to work both in a team and independently. Strong written and verbal communication skills, with a record of peer-reviewed publications. Proficiency with statistical analysis and data
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letter (maximum 1 page) explaining the motivation for the position Contact information of 3 references (Applicants are assumed to have obtained their references’ consent to be contacted for this matter
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data for model calibration and validation. Apply sensitivity analysis and optimization algorithms to refine model parameters and improve predictive accuracy. Contribute to code development, documentation
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system, network, and signal designs. Consequently, the project's main objective is to jointly consider communication and data fusion requirements in designing an efficient WSN-IoT system/network
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development. Proficiency in biodiversity monitoring tools and plant growth tracking. Field experimentation skills and experience in ecological data collection and analysis. Demonstrated hands-on experience
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Intelligence, Machine Learning, Data Science, Electrical Engineering, or a related field. Strong experience in developing and applying AI/ML models to energy systems or similar applications. Proficiency in
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calculations to model and optimize heterogeneous catalysts. Conduct simulations to evaluate catalytic performance and reaction mechanisms. Analyze and interpret computational data to understand the interactions
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: PhD degree in Materials Science, Chemistry, Crystallography Knowledge in crystallization technics Expertise in solving crystal structure using single-crystal X-ray diffraction data. Experience in