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Field
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digital twins and physical proof-of-concept models, as well as conducting field tests. About the person: We welcome highly motivated and ambitious applicants with enthusiasm for AI, Quantum Technologies and
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architecture, component selection, and experimental setup. iii. Conduct lab and site tests to evaluate the performance of NQR prototype. iv. Generate design documents and test documents at every stage
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experiments for testing and analyze DC fast charging for high power marine EVs and assess communication integrity at various operating conditions. Test interoperability of charger with marine EVs and ensure
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Qualifications: Git and modern software practices such as unit testing, CI/CD, and collaborative development. Experience with HPC environments and batch schedulers like Slurm. REST API development or integration
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benchmarks (e.g. UN SDGs) Design and testing of approach, incorporating both qualitative and quantitative approaches Development of action orientated insights and impact strategy Publication of results
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libraries (e.g. Pytorch, TensorFlow, Spacy, Transformers…) Knowledge of deep learning methods for fine-tuning large language models. Advanced programming skills in Python. Experience in software development
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the government's significant investment in artificial intelligence over the next five years (regjeringen.no) (in Norwegian). Your immediate leader will be the leader of the Software Engineering unit . About the
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of the Software Engineering unit. About the project AI LEARN is a new Centre dedicated to the empowerment of human learning through responsible, human-centred artificial intelligence. In a world where AI systems
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through the government's significant investment in artificial intelligence over the next five years (regjeringen.no) (in Norwegian). Your immediate leader will be the leader of the Software Engineering unit
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: DEI-FCTUC III- Scientific supervision/coordination of the grant: João Rodrigues de Campos IV - Work Plan / Goals to be achieved: Title: AI-Augmented Fault Injection for Dependability Testing of Software