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Field
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algorithms to understand how perturbations to dynamic brain networks impact seizure likelihood. With an emphasis on the emerging field of digital twins for personalised health, the ultimate aim is to translate
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algorithms. This work will be evaluated through simulations and with physical drones in mock search-and-rescue missions. This position will involve close collaboration with the PhD student at SDU who is
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typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical
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simulating/implementing quantum algorithms for field theories on quantum hardware. Appointment Detail: This post‑doctoral position is a full‑time, 12‑month appointment with annual renewal contingent
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working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI systems
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, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms that challenge traditional assumptions in databases, guided by
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of molecular networks • Have knowledge of molecular networking (Molecular Network (MN) and Feature Based Molecular Networking (FBMN)) and similarity algorithms: ie, Cosine score • Master the English language
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skills (Matlab, Python, C++) are a significant asset. •Knowledge of artificial intelligence techniques and associated optimization algorithms would be appreciated. •Good knowledge of English (working
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: • AI for Quantum: We develop advanced, physically grounded AI algorithms to solve fundamental problems in quantum materials, quantum error correction, and quantum simulations. • Quantum for AI: We
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motivated to move the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning