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degree (at least 240 higher education credits) in Energy Engineering, Computer Science, Robotics, or Electrical and Computer Engineering. A four-year natural science degree with technical content
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machine learning, engineering, data sciences, applied mathematics, or another related field; or Have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including
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, computer engineering, human-computer interaction, or equivalent by 2025-07. Demonstrate proficiency in English (reading, writing, speaking). Show the ability to work independently as well as in a team. Good
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, engineering physics, biomedicine, or similar Documented skills in data-driven analysis (machine learning using python with TensorFlow, PyTorch, or similar) and computational statistics Specific knowledge of big
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academia and industry. Requirements The following qualifications are required: Solid knowledge in mathematics and statistics, in areas such as linear algebra, probability theory, machine learning, high
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machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid understanding of fluid dynamics and heat transfer, as
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. To meet the general entry requirements for doctoral studies, you must: Hold a Master’s degree in computer science, image analysis and machine learning, engineering, data sciences, applied mathematics
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in computer science, engineering, data sciences, applied mathematics, machine learning, or another related field; or Have completed at least 240 credits in higher education, with at least 60 credits at
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hampers our ability to establish causal relations between molecular alterations and disease phenotypes. In this PhD you will address this by developing a deep learning model of cancer. The PhD position
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particularly valuable. Documented experience with machine learning and biostatistics is also highly meritorious.You can find information about education at postgraduate level, eligibility requirements and