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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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interactions in health and disease Microbiome-driven mechanisms in inflammatory, metabolic, oncological, and infectious diseases Development of innovative experimental model systems for mechanistic and
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learning for mathematics (e.g., model architectures for theorem proving, data-driven exploration of mathematical structures) Or experience in related areas and a passion for mathematical discovery We welcome
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), regulatory science, pharmacometrics, and real-world evidence (RWE). The successful candidate will develop AI-driven systems to support regulatory document intelligence, automated pharmacokinetic modeling, and
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UPOs PhD enrolment: Université Paris Cité DC15: Hybrid machine learning models for data-driven bioprocess optimisation PhD enrolment: University of Padua Eligibility Requirements: Doctoral Candidates
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hydrologic and hydraulic models (e.g., WRF-Hydro, HEC-RAS, OpenFOAM, GSSHA, Delft3D, EFDC, etc.). Data Engineering & Computational Workflows – 35% Curate, preprocess, and analyze large environmental datasets
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experience with process modeling with Aspen Plus Demonstrated experience with statistical analysis (i.e. sensitivity or uncertainty) PhD degree in chemical engineering or related field Preferred Qualifications
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
. This involves theory but also the integration of observational information into models through data assimilation and model inversion. In this domain as in many fields of applied science, researchers face high
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Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired, method-driven theory to application-driven research. Please find more
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, Autonomous and Interactive Systems, and Global Sustainability Engineering. Project Overview The AI Pathologist project is an interdisciplinary initiative aimed at developing an advanced AI-driven diagnostic