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Field
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at NTU are looking for a Research Fellow (RF) to carry out research in probabilistic machine learning and GenAI, by exploring cutting-edge approaches such as sequence model design, continual learning
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, Bioengineering, Physics, Chemistry, or a related field. Good publication record. Experience in one or more of the following areas: computational biophysics, biomolecular modelling, machine learning, or protein
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pathophysiological mechanisms for acute organ injury research under POI-KB. · Authorship of high-impact first-author or co-author manuscripts in leading informatics and machine learning journals and conferences
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
genetic data Multimodal machine learning for biological discovery Translational genomics and risk modeling The fellow will work in an environment that emphasizes methodological innovation, statistical rigor
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machines Machine Culture Ongoing work on social reinforcement learning and evolutionary optimization of social strategies Our aim is to advance the scientific knowledge of human-AI systems by understanding
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uncertainty. Utilize machine-learning and data-mining approaches to recommend bioengineering interventions. Develop new machine-learning algorithms. Integrate machine learning techniques with mechanistic
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intelligence, machine learning, deep learning, environmental modelling, climate-health research, early warning systems, or related fields. Have strong programming and computational skills in Python, R, MATLAB
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techniques. You will have the opportunity to participate in various projects utilizing artificial intelligence (AI) and machine learning (ML) to develop applications that optimize combat casualty care
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to the Terascale Project Knowledge of research methods relevant to AI for materials science Knowledge of one or more of the following areas: machine learning interatomic potentials, generative AI, automated and