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on embedding state-of-the-art LLMs into the set-up stage of generative shape-optimisation algorithms. The aim is to learn more expressive design-variable encodings and establish tighter, more intuitive links
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Shape Optimisation This project focuses on embedding state-of-the-art LLMs into the set-up stage of generative shape-optimisation algorithms. The aim is to learn more expressive design-variable encodings
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Leiden, which will maximize access to learning opportunities, local research networks, and access and facilities. Candidate profile: Applications are invited from outstanding graduates who meet the
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to learning opportunities, local research networks, and access and facilities. Candidate profile: Applications are invited from outstanding graduates who meet the following criteria: BSc, including synthetic
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particular in deep learning, LLM, digital hardware design, embedded systems, audio processing; Proficiency in deep learning frameworks (e.g. PyTorch) and programming skills (SystemVerilog, Verilog, Python, C
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and Chemistry at Leiden will enable the candidate to maximize their learning opportunities, participate in local research networks, and access leading research facilities. Candidate profile
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networks, or network science, and relevant background knowledge n methods in machine learning and AI. The successful candidate will focus on innovating the field of network analysis with AI methods. Examples
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-based, tailored interventions and to aid efforts to reduce the violence burden on society. As a Group, we teach a range of courses in the minor Violence Studies and in the CSM Track Governance of Violence
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. CML distinguishes itself for its attention to professional development in coordination with the interests of its scientists. As such, there are plenty of opportunities to learn new skills, expand your