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apply machine learning and deep learning models (e.g., graph neural networks, generative models, transfer learning) for materials property prediction, interpretation, and inverse design. Perform high
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on polymer chemistry. Placed at the Centre for Analysis and Synthesis (CAS) at the Department of Chemistry, the recruited Assistant Professor will be supported by funding of a PhD student. The Department
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. Applications must include a CV, a cover letter, and transcripts from Master's 1 and 2. Title : Deep-learning for nuclear data in physics for health This PhD project aims to improve the modeling of nuclear
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operation and maintenance of equipment Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer
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Welcome to Maastricht University! You just finished your PhD trajectory and looking for the next step in your academic career? Your interests lie in the field of machine learning techniques
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benefits, such as medical insurance, provident fund, on-campus accommodation/housing allowance and other subsidies. Further details on our package are available at: https://career.admo.um.edu.mo/learn-more
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
) Collaborative Context and Expertise Since 2018, EDF has been studying the construction of meta-models based on deep neural networks (i.e., deep learning). Initially, based on fluid simulations using Code_Saturne
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. Willingness to support project reporting and milestone reviews. Hard skills Strong programming skills in Python and deep learning frameworks such as PyTorch or TensorFlow. Experience in autonomous navigation
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have skills in eukaryotic cell biology, electron microscopy, and bioimage analysis. You have a basic knowledge in integrative structural biology, and in AI / deep learning approaches and/or sub-tomogram
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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