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consortium, 23 partners across Europe, aims to unlock the hidden potential of global metagenomic sequence space using a combination of synthetic biology, machine learning (ML), and ultrahigh-throughput
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comprehensive databases combining nationwide Norwegian health and socioeconomic registry data, biobanks and patient-reported data. Using advanced epidemiological methods, causal inference and machine learning
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synthetic biology, machine learning (ML), and ultrahigh-throughput screening (microfluidics) to discover new enzymes and bioactive molecules with applications in biotechnology, medicine, and sustainability
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epidemiological methods, causal inference and machine learning techniques, we aim to: Improve understanding of risk factors for primary headaches Predict diagnosis and disease progression Identify the most
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Academically relevant background within marine control/cybernetics, computer science, or hydrodynamics, with good skills in mathematics, programming, and machine learning. Master's degree in control engineering
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Academically relevant background within marine control/cybernetics, computer science, or hydrodynamics, with good skills in mathematics, programming, and machine learning. Master's degree in control engineering
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to your work duties after employment. Required selection criteria You must have a relevant Master's degree in Computer Science, Artificial Intelligence, Data Sciecnce (with a focus on machine learning
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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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Master's degree in Computer Science, Artificial Intelligence, Data Sciecnce (with a focus on machine learning) or equivalent. Your course of study must correspond to a five-year Norwegian course, where 120
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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