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Field
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at both nanoscale and macro-scale. Interaction with industrial partners that produce lignin-based adhesive to optimize the function in construction materials will also be needed. Qualifications To be
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modeling, control, and optimization of power and energy systems with applications to maritime and coastal infrastructures (e.g., shipboard microgrids, port facilities, islanded communities, desalination
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physicochemical properties of PFAS, thereby supporting environmental risk assessments and optimization of removal technologies. Responsibilities and qualifications The postdoctoral researcher will work on the
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for 1.5 years, and will be embedded within the UMC Utrecht AI-Lab initiative. Your primary focus will be the development of AI models for defect detection and process optimization of tissue constructs
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at various stages of development in Brazil, China, India, and Poland, and additional NZx studies are under discussion. Initially, the applicant's work will be primarily with the Net-Zero Brazil collaboration
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investigating adaptive neural circuits underlying distinct forms of behavioural flexibility. The postholder will be responsible for designing, developing, and operating advanced large-field or multi-brain region
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of adipose tissue function. Job information Expected start date: 01.01.2026 or upon agreement Contract duration: 1 year contract with possibility of renewal Percentage of activity: 100% Location: Lausanne
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faculty and staff working on our dynamic campus in Espoo, Greater Helsinki, Finland. Diversity is part of who we are, and we actively work to ensure our community’s diversity and inclusiveness. This is why
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seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making
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resources, and the DOE ESNET network. Develop and apply advanced workflow capabilities that improve performance, portability, and productivity. Perform performance analysis and optimization across end-to-end