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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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, and/or data-driven modeling Interest in interdisciplinary research and open science. LanguagesENGLISHLevelExcellent Years of Research ExperienceNone Additional Information Benefits The successful
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rates, • Work in a friendly, collaborative and productive environment, • Mentor and teach highly motivated students, • Be part of a mission-driven institution, • Live affordably in a central location with
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to new or emerging opportunities. Evaluate cost structures related to faculty workloads, staffing models, classroom utilization, instructional technology, and program delivery to provide insights around
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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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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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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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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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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 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