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engineering or a closely related field. Hands-on expertise – You have experience with OoC or MPS technology, computer-aided design, microfabrication and ‑fluidics, culture of human cells, analytical cell and
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explain AI's internal mechanisms by generating understandable explanations from machine learning models. We will achieve this together by creating the first mathematical framework for explainable AI and
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mechanics, from probing the quantum-to-classical transition to investigating the quantum nature of gravity. You will be jointly supervised by the Rossi Lab and the Conesa-Boj Lab, and become part of a diverse
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Understanding the nature of enzyme promiscuity whilst enabling a more
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, Engineering or related field. A strong background/knowledge in machine learning and computer vision, natural language processing is a plus. Solid mathematics foundations, especially statistics, calculus and
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technology, computer-aided design, microfabrication and ‑fluidics, culture of human cells, analytical cell and molecular biology techniques, and bioimaging methods. Analytical and proactive – You combine sharp
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, natural language processing is a plus. Solid mathematics foundations, especially statistics, calculus and linear algebra; Excellent programming skills, preferably in Python. Experience with AI/HPC
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challenges in the field. Your research will contribute to advancing our understanding of macroscopic quantum mechanics, from probing the quantum-to-classical transition to investigating the quantum nature
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research project funded through the Horizon Europe funding scheme of the European Commission, entitled “Hazard assessment and risk management of natural hazard-triggered Technological (NaTech) emergencies
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, in consultation with the supervisors. - Produce a PhD thesis, written in English, consisting of four data chapters, an introduction and a discussion at the level of international scientific journals