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Field
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is seeking a PhD student to join an ongoing Royal Society–Research Ireland University Research Fellowship project focused on Machine Learning for the Design of Additively Manufactured Two-Phase Heat
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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learning, particularly flow-matching generative models and protein language models. The research will focus on designing efficient generative models able to produce realistic conformational ensembles while
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for projections. This project aims to explore the coupling of the ocean and ice-sheet model components via a machine learning emulator of ice-shelf cavity circulation. While the ultimate goal of the project is to
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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experience in Python and, more specifically, in common deep learning frameworks such as PyTorch and jax, for model training and inference have experience with embedded platforms such as FPGAs or RISC-V
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as PhD candidate in the field of machine learning for materials science. Your immediate leader will be the Head of Department. About the project Can AI interpret graphs like a human materials scientist
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learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development of new data-driven
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two clinical studies in Kenya and Zambia, assessing whether E. coli carriage contributes to Enterobacteriaceae colonization and ARG transmission. Finally, the PhD student will use agent-based modeling
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
., Revenko, A., Teije, A. T., & Harmelen, F. V. (2023). Combining Machine Learning and Semantic Web: A Systematic Mapping Study. https://doi.org/10.1145/3586163 [2] Benoît Combemale, Pascale Vicat-Blanc