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; expertise in several of the following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and
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dedicated researcher with a PhD in mathematics, electrical or mechanical engineering, computer science or a related field, and a strong methodological background in machine learning. The ideal candidate has
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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
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, linear mixed models, or deep learning architectures). A solid theoretical background in literacy development, child language acquisition, or psycholinguistics. A record of academic publishing and high
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. About You You will have a PhD in computer science, engineering, mathematics, or similar, strong programming skills, and experience with a contemporary machine-learning framework such as PyTorch. You will
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. About You You will have a PhD in computer science, engineering, mathematics, or similar, strong programming skills, and experience with a contemporary machine-learning framework such as PyTorch. You will
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, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply
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environments; (2) identify which patterns of student-AI interactions influence the adoption of deep or surface approaches to learning; (3) create, implement and evaluate guidelines and knowledge base
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Do you want to combine high-throughput directed evolution with machine-learning analysis of deep sequencing data to engineer better antibodies? The Sormanni Lab in the Department of Chemical
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school, and taking part in the supervision of students. A limited amount of teaching may be included (max 20%). Requirements PhD degree in machine learning, scientific computing, statistics, physics or a