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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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Apply now Job no:537640 Work type:Full Time Location:Sydney, NSW Categories:Information Technology, Analyst, Cyber Employment Type: full time continuing role Specialist II, Security Engineering
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pioneer of purpose-driven science and Cornell University’s second-largest college. We work across disciplines to tackle the challenges of our time through world-renowned research, education, and outreach
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contributes $10.6 billion in economic impact to the city and $22.7 billion to the state of Ohio. At UC, next is all of us. Learn more at uc.edu . UC is a mission-driven organization where we are committed
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goal is to implement AI-driven solutions in textile engineering, fashion and textile design, textile management and business-oriented IT, with the aim of promoting a green transition in the textile and
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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
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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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unique biological systems. To achieve this goal, we are mining novel biological systems from databases using AI-driven tools, characterizing their functions through biochemical analysis, engineering them
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2 Mar 2026 Job Information Organisation/Company Ecole Centrale de Lyon Research Field Chemistry Computer science Engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions
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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