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Field
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on machine and deep learning methods for analyzing the heterogeneity of microbiota and inferring activities of biological pathways. The Institute provides an international and interdisciplinary research
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, training deep learning models on large satellite datasets, and contributing to the integration of forecasting workflows into the IceBox system. The candidate will work closely with an interdisciplinary team
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Doctoral Researchers (PhD students) to work on deep learning methodologies for machine and robot perception. These positions are funded by the Horizon Europe project OPERA (Open Perception, Learning, and
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). Deep learning has been used to perform this mapping from UAV (Batista et al., 2025; Chudasama et al., 2024; Lambert et al., 2025) or satellite imagery (Mattéo et al., 2021), at both very high resolution
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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, and train deep learning models on the resulting data to design new antibiotic compounds that evade both current and likely future resistance mechanisms. Your computational work will directly steer
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international work environment Learn more about CQT at https://www.cqt.sg/ Job Description The successful candidate will drive research at the intersection of Condensed Matter Theory, Quantum Computing and
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discipline Strong experience in integrating several of the following components: Deep learning and LLMs for molecular biology Vision foundation models for pathological image analysis Multi-omics datasets (e.g
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analysing multimodal deep learning models for time-specific cancer risk and time-to-event prediction by integrating imaging with longitudinal Electronic Health Record (EHR) signals. Building scalable
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation