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and regions involved in protein interactions. In recent years, the field has moved from the analysis of purified complexes to proteome-wide studies thanks to fundamental instrumental and bioinformatic
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teams or pursue their own research project or vision relevant to the department’s profile. The department develops mathematical, statistical, and computational methods for the analysis of complex systems
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analysis of complex biomedical datasets. The research environment is highly interdisciplinary, bringing together expertise from machine learning, statistics, data science, and healthcare research
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analysis of complex biomedical datasets. The research environment is highly interdisciplinary, bringing together expertise from machine learning, statistics, data science, and healthcare research
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applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials
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experience is desirable Essential skills: Strong expertise in microbiological techniques Proven experience in the purification and analysis of protein complexes Experience in structural biology, ideally in
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pioneering method for obtaining the full conformational landscapes of biomolecular complexes at atomic scale from cryo electron microscopy (cryo-EM) images. It has been shown that MDSPACE analyzes
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to innovative research at the heart of current environmental challenges. You will be responsible for the physicochemical analysis of complex samples of environmental interest, such as secondary organic aerosols
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studies and experimental research examining how managers formulate strategic problems and make decisions in complex organizational environments, as well as how artificial intelligence influences diversity
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on complex problems involving the development of new theories and methodologies. The research will be largely focused on the development of predictive computational tools for the analysis of the spatial spread