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Field
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computational models, applying statistical and machine learning methods, and integrating data across modalities to generate novel scientific insights. The Postdoctoral Fellow will lead manuscript preparation
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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, and machine learning. The environment at GBI will allow researchers to undertake ambitious, long-term, collaborative research, and we will actively support the translation of research to commercial
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, etc.) o Energetic frustration or protein energy landscape analysis o Machine learning in protein science o +2 years of experience after PhD Knowledge of evolutionary biology concepts (phylogenetics
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from different fields: AI (machine learning, big database, etc) Semiconductors
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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather
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. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components. The aim is that fault diagnosis
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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of atomic environments Detection of extrapolation and low-reference data regimes Active learning in configurational and chemical space Training and benchmarking of large-scale foundational MLFF models More
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-performance computing resources suitable for large-scale machine-learning and foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025