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: Development of ML-based tools for analysis and engineering protein dynamics PhD enrolment: Czech Technical University in Prague DC14: Machine learning for Empirical Valence Bond (EVB) simulations to engineer
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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modelling predictions. Experience or a strong interest in scientific programming and machine-learning-assisted data analysis for materials modelling is an advantage. PhD Position 2 – Coarse-Grained and
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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures
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team players and passionate on cutting edge computer vision and machine learning technologies, as well as possess deep understanding of machine learning technology and experience on turning machine
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systems, and space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where
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contributions as a software engineer for a wide range of projects requiring computing systems design and realization, including machine learning (ML) and artificial intelligence (AI) applications including
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 2 days ago
include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search
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15 Jan 2026 Job Information Organisation/Company IFP Energies nouvelles (IFPEN) Research Field Engineering » Mechanical engineering Researcher Profile First Stage Researcher (R1) Positions PhD
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an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology Machine Learning, AI Safety, AI Alignment, Eval of LLMs, Multi-agent Safety