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intelligence, computer science, remote sensing, geomatics, data science, or a forest/environmental science discipline with a strong quantitative or AI component Strong knowledge of machine learning and deep
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degree in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, or a closely related field. A strong academic record and solid background in machine learning and deep learning
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Intelligence, Machine Learning, Natural Language Processing, or a closely related discipline. Demonstrated expertise in Large Language Models (LLMs), Natural Language Processing, Efficient Deep Learning, AI
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discipline. Strong background in machine learning, deep learning, and large language models. Experience working with large-scale datasets and distributed model training. Strong programming skills in Python
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flexible fitting of mechanistic and statistical models to the data. Our current focus is on generating datasets of sufficient size and diversity to train machine learning models to accurately predict how
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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Computer Science, Artificial Intelligence, Mathematics, Engineering, or a related field. Entry level candidates with demonstrated expertise in artificial intelligence (AI), machine learning, deep learning
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. The following experience will strengthen your application: Experience in deep learning and probabilistic machine learning Experience in optimization and message-passing algorithms Previous experience in research
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analysis, or assistive technology; Experience in applying AI methods such as machine learning, deep learning, computer vision, multimodal data analysis and large language models (LLM) in health-related
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Intelligence and Data Analytics in Air Traffic Management Systems. The selected candidate will work on developing innovative machine learning models to address key challenges in the future airspace system