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Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs. The candidate will apply their expertise to advance predictive maintenance systems using AI
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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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) 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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; Experience in developing and validating methods, operating and troubleshooting different gas/vapor adsorption/membrane testing instruments; Willingness to learn how to generate new ideas, links, and to build
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
, and Precision Health. The project aims to leverage AI and machine learning (ML) to analyze complex metabolomics datasets and address key health challenges, including biomarker discovery, disease
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. Applications and selection procedure: Applications must be sent using a single electronic zipped folder with the mention of the job title in the mail subject. The folder must contain: A 1-page cover letter with
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activities. Qualifications: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural
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photovoltaic installations in semi-arid post-mine environments. The objectives include soil stabilization, dust reduction, and the creation of a favorable microclimate to enhance the energy performance of solar
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(especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. Advanced skills in predictive modeling and machine learning, particularly for multi-variable simulations. Knowledge of complex systems
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in energy to join our research team as a Postodoctoral researcher. The position focuses on studying the performance of photovoltaic solar panels installed on post-mining sites, in relation