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and application of Large Language Models (LLMs), to join our team working on predictive maintenance solutions. The ideal candidate will have recently completed (or be close to completing) a PhD in
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very low; Propose characterisation of the soil properties collected from different studied farms; Test how to Improve soil organic carbon content using organo-mineral resources under controlled condition
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farms; Test how to Improve soil organic carbon content using organo-mineral resources under controlled condition; Assess the effect of using organo-mineral resources on soil carbon stock in the field
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mentor MSc and PhD students involved in related research activities, providing guidance in experimental design, data analysis, and manuscript preparation. Contribute to grant writing, proposal development
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variable. The means between land uses will be compared by Tukey test (p<0.05). Criteria of the candidate: PhD in environmental science, soil science, surface geochemistry, or related fields from a recognized
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publish in leading international journals. Desired skills and experiences: PhD in crop physiology, microbiology, plant molecular physiology, or related fields. Strong knowledge about plant-soil-microbes
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validation to support model accuracy. Publish research findings in peer-reviewed journals and present results at conferences and workshops. Mentor master students and PhDs Qualifications: Ph.D. in Remote
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present results at conferences and workshops. Mentor master students and PhDs Qualifications: Ph.D. in Remote Sensing, Geospatial Science, Environmental Science, Data Science, or related field. Strong
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recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs
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proposals and presentations. Requirements: Specify the required qualifications and experience for the position. This might include A PhD in a relevant field such as mechanical engineering, materials science