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Transformers). Analysis of existing datasets. Evaluation of the trained models on suitable datasets. What you contribute Good knowledge in the field of machine learning and training neural networks. Good Python
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epitaxy (MBE) Develop innovative approaches to accelerate MBE materials development using in situ characterization & machine learning Integrate epitaxial synthesis with exfoliated two-dimensional materials
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The Department of Accounting at the Rutgers-Camden School of Business invites applications for a graduate-level instructor to teach Accounting Information Systems in the fall of 2026. This course prepares students
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: Collaborate and participate in large consortia to assess needs and requirements Design and develop machine learning models for genomics applications Maintain and update existing deep learning frameworks
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to university departments on Campus Solutions data models and the government regulations. Support the Campus Community module. Gather and document business requirements, Technical Consultation and Project
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, designing, implementing, and evaluating ML models that address practical challenges across domains. The researcher will contribute to the development of a full machine learning pipeline, including data
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Linguistics, Analytics, Search, and Informatics Documented opensource contributions Experience with NLP/deep learning frameworks such as pytorch and tensorflow Experience with NLP state-of-the-art models and
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on artificial intelligence techniques, namely machine learning and deep learning; (3) analysing mathematical models applicable to renewable energy generation technologies and electrical energy storage systems; (4
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Science, Computer Science, Data Science, Neuroscience, or a related field by the start date. Demonstrated expertise in computational modeling of human behavior or computer vision / machine learning
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 19 days ago
was installed at IT4Innovations in 2025. For more details, see www.it4i.eu . Activity description: · modelling and optimization of electrical networks using open source tools (preferably Julia or Python