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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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, qPCR, sequencing) for the identification and phylogenetic analysis of microalgae and target pathogens. Supervise and mentor MSc and PhD students involved in related research activities, providing
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Knowledge of biogeochemical cycles Experience with process-oriented modeling Selection Criteria PhD degree or Master degree in a field such environmental sciences, agronomy, applied mathematics
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presentations; Collaborate with internal teams, partner laboratories, and industrial stakeholders. Candidate Profile: Required qualifications : PhD in Mineral Processing, Hydrometallurgy, Chemical Engineering, or
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Profile: Required qualifications : PhD in Mineral Processing, Hydrometallurgy, Chemical Engineering, or related discipline; Proven experience in ore characterization and process development; 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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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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required PhD in one of the following fields: electronics and embedded systems, telecommunications, industrial computing, electrical engineering or equivalent. Expertise in the design, prototyping and
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candidate must have as well a multidisciplinary scientific profile: PhD degree in materials science, chemical engineering, chemical Processes or another suitable engineering discipline. Knowledge about
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motivated candidate with a PhD from a recognized University who is passionate about developing innovations that have direct impacts on farmers in arid lands. The candidate will participate in evaluating plant