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academic development are top priorities. Our people are committed to expand fundamental knowledge by curiosity and to look beyond the borders of their own discipline; their aim is to benefit science, and to
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limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data
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workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and limitations. The field of interpretable machine learning aims to fill this gap by developing
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PhD position understanding and designing nanomedicines-membranes interaction: physics based approach
the nanomedicines with high specificity to the desired target site in the body. In this PhD project, the candidate will experimentally study how to achieve high specificity by developing a quantitative understanding
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of 16 academic & industrial partners will train 14 PhD fellows (3 of which are to be hired through this call). What GenAIDE offers you: A structured, four-year PhD training programme that mixes
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Institute of Chemistry are looking for a PhD candidate to pursue research on the development of host-directed therapeutics for mycobacterial infections. Mycobacterial infections are among the top 10 cause
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is to deliver the nanomedicines with high specificity to the desired target site in the body. In this PhD project, the candidate will experimentally study how to achieve high specificity by developing
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of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data