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, Abstraction and Reasoning, Bio-Inspired and Neuro-Inspired AI, Artificial Evolutionary and Developmental Systems, Alignment, Social Learning and Cultural Evolution, and other Artificial Life techniques
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physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidates will work in close cooperation with our current PhD students within the PhD programme, and
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expertise in the following areas: Machine Learning in general, with an emphasis on deep learning and language modeling Model benchmarking and evaluation pipelines for NLP/LLMs Domain-aware application of AI
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spatial analysis and mapping tools (e.g., QGIS, ArcGIS, or spatial packages in R/Python) Interest or experience in applying AI or machine learning methods to ecological questions Personal attributes: Strong
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will be to: teach and supervise at bachelor’s and master’s levels help develop the department’s study and degree programs perform academic administrative tasks be a driving force in the development
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challenge. This project aims to explore data-driven Artificial Intelligence/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines
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inference methods, survey design, and/or machine learning Experience with web scraping and API-based data collection Organizational and coordination skills, such as assisting in drafting terms of reference
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(written and spoken) The successful candidate is expected to move to Bergen Desired Qualifications: Experience with Stata Experience with causal inference methods, survey design, and/or machine learning
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large datasets, and applying AI approaches (e.g. machine learning, image segmentation, multimodal AI data integration) will be considered advantageous. Strong skills in communicating scientific results
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efforts to contribute to safer marine operations, we actively explore possibilities to utilize both numerical and machine learning methods to enhance the accuracy and resolution of metocean forecasts. About