11 molecular-modeling-or-molecular-dynamic-simulation Postdoctoral positions in Italy
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of lung cancer genomics, transcriptomics, and molecular biology. The successful candidate will join a young, dynamic, and interdisciplinary team dedicated to uncovering the molecular mechanisms driving lung
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Model. Adv. Sci. 2025, 2500400. https://doi.org/10.1002/advs.202500400 Research Area Applications are invited from outstanding candidates with expertise in molecular biology, biotechnology, and/or
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into the vegetation. Within this European project, the University of Trento (Department of Information Engineering and Computer Science) leads the research activities related to electromagnetic modeling and digital
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knowledge in the field of ecosystem dynamics and computational oceanography, through the development or application of theoretical, numerical, or modelling approaches for the study of physical and
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representations and state sequences for predicting disease progression and outcomes; - Developing advanced generative models to simulate patient health trajectories, including disease progression, based on real
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. The project will be carried out in a stimulating biology- and molecular medicine-oriented research environment shared with multiple principal investigators capable of attracting several important international
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Responsibilities Conduct foundational research in adversarial machine learning, exploring novel attack vectors and defense mechanisms for AI agents and large language models. Develop formal verification methods and
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: (i) molecular cloning (ii) pooled genetic screening (iii) flow cytometry (iv) mammalian tissue culture (cells and/or organoids) good command of the English language knowledge of Italian is an
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, spectral matching against databases (e.g., Metlin, KNAPSAcK), and statistical modeling for biomarker discovery. Integration and Interpretation: Correlate metabolomics data with biological and geographical
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, communication algorithms) Parallel programming models and runtimes for emerging architectures (e.g., processing-in-memory, SRAM-centric) Performance modelling & optimisation of ML algorithms on unconventional