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Field
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their research at the state-of-the-art facilities of the KIOS Center of Excellence in one or more of the following thematic areas: Control systems, computational intelligence and machine learning, autonomous
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Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong
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interest in data analysis, modelling, statistics, and machine learning. Experience in spatial data analysis (GIS), scientific programming (Python, R, or equivalent), or artificial intelligence will be
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control, statistical analysis and critical interpretation of results.. Preferred Factors: Will be considered preferred factors: -• Knowledge of machine learning and advanced data analysis, including
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20 Aug 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile
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University of Girona (UdG) - Institute of Computational Chemistry and Catalysis (IQCC) | Spain | 2 months ago
(CompBioCat) research group at the Institute of Computational Chemistry and Catalysis (IQCC, http://iqcc.udg.edu ), University of Girona (Spain), is seeking a highly motivated PhD candidate to join the ERC
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imaging. Experience with scientific programming (e.g., MATLAB, Python and/or C++). Excellent analytical and problem-solving skills. Interest in image reconstruction, beamforming, machine learning, and
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of ecosystem degradation and the aforementioned extreme climatic phenomena; (v) explore a “Machine Learning” analysis to explore the importance of other environmental and managerial factors in the spatial and
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: Development of a computer vision-based machine learning model, using Convolutional Neural Networks or similar architectures, for the early detection of diseases in pome fruit orchards. Tasks: - Review machine
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Campylobacter disease burden is assessed, identify drivers of change and possible interventions. You will explore how genomic diversity relates to clinical outcomes, whether machine‑learning approaches can