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(e.g. python, R); affinity and experience with performing animal experiments; experience with classical biological methods (e.g. dissections, immunohistochemistry, western blot, PCR, in vitro assays
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The overall aim of the project is to resolve the quantum trust challenge where the inherently quantum nature of these devices, being beyond classical simulation, complicates their straightforward validation
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optimisation. 2 – Explainable AI with LLMs for Transparent Decision-Making in Design Engineering This project combines classic XAI methods with LLM capabilities to generate human-readable explanations during
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condensate formation by exploring different RNA association modes. By combining classic phase separation assays and single molecule reconstitution, you will address the following questions: How does the eRNA
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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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and materials research that could not be addressed so far due to their high complexity, which prevents approaches that solely rely on classical mechanistic modeling or classical machine learning. Equal
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be placed on curiosity-driven explorations of unconventional light–matter interactions in those systems, and their understanding in the framework of classical and quantum optics, in combination with
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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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Organisation Job description Thanks to the generosity of the Sent Marks Fonds we are able to invite applications for a fully funded, 4-year PhD position in the field of Classical Literature
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specific drug resistance and pathogenesis mutations. The project will combine classical microbial genomics with machine learning and AI analysis approaches to create the most in depth population analysis