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: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
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(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. The candidate will apply
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expertise in research and development in the following areas of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in
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staff position within a Research Infrastructure? No Offer Description Job Description We are seeking a highly motivated and skilled post-doc in in Chemistry, Materials Science or related fields to join
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at conferences, and stakeholder engagement sessions. Required Qualifications: A Ph.D. in Climate Science, Hydrology, Environmental Science, or a related field. Experience in machine learning or AI applications in
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techniques (FTIR, NIR, Raman, …) with machine learning/ chemometrics. Responsibilities of the Position The Postdoctoral researcher is intended to support the soil spectroscopy research activities and digital
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models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical
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machine-learning-driven multi-criteria decision analysis to rank and select optimal decarbonization pathways. Collaborate with industry and academic experts to ground-truth results. Dissemination Publish in
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-based observations, physical algorithms, and machine learning models. Participate in field data collection and validation to support model accuracy. Publish research findings in peer-reviewed journals and
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oriented institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development