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, RNAseq, serum proteome, WSI), incorporating the clinical data, primarily for the identification of biomarkers that predict response to treatment. The spatial studies will be a key focus, but integration
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, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical composition and atomistic modeling
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of predictive models for patient stratification and disease progression Computational disease modelling and target prioritisation linked to lipid metabolism Translation of bioinformatics results toward clinical
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methods, large-scale data resources, and AI-assisted frameworks for understanding microbial function. Ultimately, the research aims to enable predictive and data-driven insights into microbiome systems and
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to correct or account for these biases, and build predictive models that simulate biological responses to in silico perturbations such as genetic or pharmacological interventions. The project aims to advance
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to accelerate the path to certification. More details on the project can be found here: https://hecustom.eu/ This post will contribute to the creation and validation of a digital twin (with biological bone models
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and with the 2AT team at Institut Pprime to develop an innovative jet-noise prediction tool. The researcher will develop a novel jet-noise prediction tool based on a resolvent analysis of the Navier
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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real-world field data. The central research question of this thesis is: How can Extreme Value Theory (EVT) and Bayesian Networks (BN) be coupled to build a predictive and dynamic model of NaTech risk
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-funded DECIPHER-M consortium (9 partners, €9M), we are building multimodal foundation models that integrate imaging, text, and structured clinical data to predict metastasis risk and identify tumor origin