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Field
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geophysical investigation, geomechanical engineering, and machine learning. The Centre for Urban Solutions is to provide leadership in developing innovative solutions and sustainable technologies for space
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. Qualifications and personal qualities: Applicants must hold a PhD or equivalent doctoral degree within atmospheric physics, meteorology, climate science or machine learning. PhD-students may apply if defence of
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, Artificial Intelligence (AI) & Machine-Learning (ML) applications. Good written and oral communication skills Proficiency in power system modelling, advanced control theory (e.g., model-predictive control, etc
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interactions studies. The focus of the PhD-thesis needs to contain knowledge areas such as learning theory, cognitive theories with applications on studies of learning, design of learning environments, design
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internationally through conferences, seminars, and workshops. Translate findings for diverse audiences while maintaining scientific rigor. Qualifications: PhD in artificial intelligence, machine learning
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theories in inverse/scattering problems. Once successful, the candidate is expected to transfer their expertise to machine learning/scientific computing in collaborations with other group members (phd
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sustainable manner. Key Responsibilities: Responsible for developing explainable machine learning algorithms for Tunnel Boring Machine (TBM) tunnelling and excavation Developing large language model enhanced
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. Project description The PhD fellow will be affiliated with the Scientific Computing and Machine Learning (SCML) research group at the Department of Informatics and supervised by associate professor Anne
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may include but are not limited to: algorithm and software development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi