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Field
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should have an interest in model-based design and analysis techniques. They will apply and develop formal methods and knowledge representation techniques for industrial systems. The candidate will acquire
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(R2) Positions Postdoc Positions Application Deadline 15 Mar 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Hours Per Week 37,5 Is the job funded through
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market design Transportation systems modeling and optimization Responsibilities: Postdoctoral fellows will: Develop, train, validate, and benchmark AI/ML models using large-scale, real-world datasets and
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inflammation and its role in breast cancer risk and progression. Major approaches include: Preclinical studies using mouse and rat models of obesity-associated tumor development. Evaluation of lifestyle and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Posting Information Posting Details Department SOP-DPET - 452200 Posting Open Date 08/22/2025 Application Deadline Open Until Filled Yes Position Type Postdoctoral Scholar Position Title Postdoc
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initial measurements on signal to noise optimisation. The successful applicant will be an integral part of the team comprised of the PI, PostDocs and PGR students, and will assist with the training and
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Fabrication of simple electronic and nanophotonic devices Optical and electrical characterization of quantum emitters Defect modeling and identification The PhD research fellow will be affiliated with the Solid
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including multiple and embodied AI agents. Countering the current trends of very large models with hard-to-control outputs, we will focus on balancing data-based approaches with artists’ knowledge and search
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essential to investigate the determinants of normal development in order to understand abnormal behaviour (or illness). FANS academics play a critical role connecting expertise in basic animal models through
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, sensitivity to modeling choices, and complementary empirical tests as essential components of trustworthy unsupervised learning in high-dimensional settings. Causality will be used as one analytical lens