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Field
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model species (e.g. Culex pipiens) to test specific hypotheses about sensory-driven flight behaviour under controlled light and visual environments. This will allow you to explore the underlying
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behaviour under controlled light and visual environments. This will allow you to explore the underlying mechanisms that shape swarming rules and to validate your field-based findings. The broader aim
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intuitive links between search parameters and the resulting geometry or performance outcomes. The result will be an LLM-driven framework giving designers clearer cause-effect control over generative shape
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skills in algorithmic design; Possess good communication skills and an excellent command of English. Additional Information Benefits We encourage high responsibility and independence, while collaborating
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control over generative shape optimisation. 2 – Explainable AI with LLMs for Transparent Decision-Making in Design Engineering This project combines classic XAI methods with LLM capabilities to generate
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control over generative shape optimisation. 2 – Explainable AI with LLMs for Transparent Decision-Making in Design Engineering This project combines classic XAI methods with LLM capabilities to generate
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team spirit. Furthermore, a strong command of (scientific) English in speech and writing is essential (EUR requirements minimum IELTS overall score of 7.0 [comparable to TOEFL PBT: 600; TOEFL CBT: 250
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problem-solving skills. Commitment to rigorous scientific standards and accurate record keeping Time management and ability to work independently Excellent command of written and spoken English
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for advancing healthcare innovation. By enabling high-throughput synthesis, precise control over nanoparticle properties, and scalable manufacturing, automation accelerates the development of novel nanomedicines
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command of written and spoken English (demonstrated through prior publications, theses, presentations) Strong teamwork mentality Key responsibilities: Independent research including planning experiments