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cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and demonstrated ability to effectively
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dysbiosis drives immune dysregulation and disease progression in pediatric patients, generating new clinical multi-omics data and using deep learning, structural equation models, and causal inference
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language processing that address concrete problems and are both theoretically rigorous and interpretable. The PhD is funded by the ERC CoG PANDORA (Deep Multimodal Learning for Mining and Generation of Arguments
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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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learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop and train deep learning models (CNNs, ...) data to predict IPLSs
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methods tailored to ILC. The PhD researcher will fine-tune and benchmark pathology foundation models using multi-site H&E and immunohistochemistry whole-slide images. The aim is to learn representations
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concepts into accessible, domain-relevant learning experiences for students. The full job description is available here: https://computing.mit.edu/lecturer/ Job Requirements REQUIRED: PhD in Computer Science
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. Kindly submit your CV and motivation letter to the attention of Mr. Menno Koeslag, HR Advisor. Where to apply Website https://www.academictransfer.com/en/jobs/364035/phd-candidate/apply/ Requirements
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. In this PhD project, you will investigate foundation models for automotive imaging radar. The goal is to learn general radar representations from largely unlabelled data that can generalize across
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qualifications include: Strong research experience in deep learning and foundation models, including experience with pre-trained models, fine-tuning, transfer learning, or self-supervised learning. Experience with