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Field
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experimental studies, mechanistic modelling, time-resolved data analysis, and machine learning to develop and validate predictive models linking process signals to reaction behaviour, progressing from controlled
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University safety requirements. Machines and Equipment: Operational knowledge of laboratory equipment for mechanical testing of concrete materials Operational knowledge of equipment for 3D Printing of concrete
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/or oceanography; or experience working with numerical simulations, large data, scientific programming, and/or machine learning. Applicants are asked to send a CV, a brief statement of research
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collection, data organization, and project execution to publication. Other Duties Other duties as assigned. Additional Information Machines or equipment used in the performance of essential duties Computer
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and/or case-crossover analyses, and have a promising publication record. Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization
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to industry and developments with Deep Learning (DL), Computer Vision (CV), Transformers, Large Language Models (LLMs), Natural Language Processing (NLP). Mandatory requirements • Bachelor's degree
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learning as well as a strong background in scientific programming (in languages like Julia, Python, Fortran or C/C++). The applicant must hold a PhD in physical oceanography, atmospheric sciences, computer
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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written proficiency in English. Excellent communication skills, ability
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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and