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leverage graph structures to represent, integrate and analyze multi-modal data, employing advanced machine learning techniques to address complex questions in biology. The team is focusing on different
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estimators, or machine learning) or other advanced statistical modelling. Advanced programming skills in Stata, R, Python or a similar software. Strong academic background with publications in international
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this will include: Demonstrated expertise in data analysis and simulation Familiarity with C++; and proficiency in the use of ROOT and Geant4, and interest in machine learning techniques Knowledge
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applications* in close collaboration with other discipline experts (software, microelectronics and applications engineers). * except for RF payloads. ** including artificial intelligence and machine learning
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Pneumatic Tires, Structure-Process-Properties Relationships. How will you contribute? Do you have proven skills in data analysis, machine learning, as well as in mathematical and computational modelling? You
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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. Responsibilities include working with digital signal processing, advanced filtering techniques, dynamic feature extraction, time-domain and frequency-domain analysis, signal fusion and machine learning to enhance
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on machine learning solutions and data visualisation. In addition will some cod individuals be tagged, and their behaviour be monitored using acoustic telemetry. The cod behaviour could also be correlated with
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processes and artificial intelligence and machine learning methods. The goal of Geophysicist.AI is to develop a scalable artificial intelligence ecosystem that integrates large language and physics-informed
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The postdoc fellow will conduct research in the intersection of AI/Machine Learning and Software Technology. The advertised position will be placed in the DISTA research group (https://lnu.se/en/dista