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Field
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Systems Engineering, driving forward computer-aided and experimental approaches to the modelling, simulation, analysis and optimisation of sustainable process engineering processes, incorporating
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of AI and machine-learning Develop and execute methods for characterizing and linking the texture and structure of alternative protein foods Design, execute, and interpret experiments independently
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; data assimilation; scientific machine learning and AI applied to wildfire prediction; smoke and fire-atmosphere interactions; and experimental or observational approaches supporting wildfire model
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are required. Knowledge and skills in statistics, data analysis, machine learning, conceptual modelling, knowledge engineering, and programming are also required. A first experience with Human-Centered
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/PhD degree in Computer Engineering, Computer Science, Artificial Intelligence, Data Science, Electrical Engineering, or related disciplines. Experience in project management, research administration
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are especially excited to hear from candidates eager to invent new tools, wetlab approaches for circulating nucleic acids, interpretable machine learning for biomarker discovery, and methods we haven’t imagined
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in all these approaches. This PhD position aims at developing methods for learning and control in complex large-scale systems. This will be carried out as part of the GreenControl project, whose
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should possess: PhD/Ms/BSc in Computer Science, Artificial Intelligence, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep
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, Mathematics, Engineering or a related discipline, with an interest in AI applied to complex and safety-critical systems. Good knowledge of Machine Learning and Deep Learning methods, including experience with
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employees and conducts research and teaching mainly in electrical engineering and computer technology. We are located on LTH's campus in northern Lund. Within the Division of Electromagnetics and