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Field
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mixing. Publishing research in leading journals and conferences in speech, audio, and machine learning, and contributing to open-source releases of software, trained models, and reproducible research
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. Key duties involve driving foundational research and system development in Edge AI and decentralized learning architectures. Responsibilities encompass conducting independent systems-level research
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curation workflows, or handling noisy, imperfectly labelled historical data. *Practical knowledge of software engineering best practices, containerised deployment, or scalable data architectures
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and in computer architecture domains, our ability to analyze data still falls behind the unstoppable data collection rates. Data-intensive applications are increasingly more demanding in sophisticated
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porous architectures for CO₂ capture to photoactive systems for sustainable solar-energy conversion and unprecedented photomedicines? In this project, you will build a self-driving laboratory platform
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to the project): PhD in Computer Science, Distributed Software Architectures, Artificial Intelligence, or a related discipline. Strong research background in data management, including policy governance or data
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understanding of parallel application development techniques (parallel programming models, algorithms, and software) Preferred Qualifications: Experience in implementing ab initio simulation codes such as VASP
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How can we accelerate the discovery of next-generation materials — from porous architectures for CO₂ capture to photoactive systems for sustainable solar-energy conversion and unprecedented
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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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data processing using ImageJ/Fiji or equivalent software. Knowledge of amphiphilic block copolymer self-assembly is highly desirable. Experience in integrating molecular catalysts, nanoparticles