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mobility in mining. Candidate Profile PhD (complete or imminent) in Renewable Energy, Electrical Engineering, E-Mobility, Mining Engineering, or related field. Proven publication record in sustainability
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in photosynthesis rather than a stoichiometric limitation of tissue formation. EGUsphere, 1-27 Dessert et al. (2025), Geochim. et Cosmochim. Acta 171, 216–237 Du, E., Terrer, C., Pellegrini, A. F. A
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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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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) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation
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at one of OCP Group’s production sites. The project will rely on Operations Research and Machine Learning approaches. The objective is to redesign the extraction methods by considering their impact on
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in photosynthesis rather than a stoichiometric limitation of tissue formation. EGUsphere, 1-27 Dessert et al. (2025), Geochim. et Cosmochim. Acta 171, 216–237 Du, E., Terrer, C., Pellegrini, A. F. A
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. The primary objective is to design robust and efficient planning solutions—integrated within a digital twin—that account for the uncertainties and variability inherent in industrial processes. Machine learning
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. Strong knowledge of heterogeneous catalysis and computational chemistry. Experience with computational modeling (i,e, DFT, and/or molecular dynamics). Familiarity with software such as Material Studio
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advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning