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approach of data-driven membrane discovery that includes material space construction and exploration, candidate selection and verification, providing data for machine learning models to optimise membrane
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for the position. Preferred selection criteria Scientific publications are an advantage Experience in research project works Good knowledge and experience in the use and development of machine learning algorithms
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distribution modelling Experience with 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
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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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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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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
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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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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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patients Experience with clinical data collection Familiarity with epidemiological methods and registry-based research, epigenetic analyses or machine learning. Interest or experience in science
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registry-based research, epigenetic analyses or machine learning. Interest or experience in science communication and public engagement Experience with publishing biomedical papers Experience with open