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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
of two main parts: Improve methods for automatically aligning ontologies and linking data by leveraging the scalability, approximation, and multi-viewpoint capabilities of deep learning methods. Study
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an advantage: Deep learning or machine learning Structural bioinformatics Protein structure prediction and modelling Molecular simulations Membrane proteins or membrane biophysics Scientific software development
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-style models. Pretraining may use self-supervised, contrastive, masked-modelling, or generative objectives. Own research ideas are strongly encouraged. Your responsibilities • Develop deep learning
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hyper elastic materials 2. Design optimisation for programmed mechanical response Perform size, shape, or topology optimisation Combine with deep learning-based optimisation approach Additive
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Stage Researcher (R1) Positions PhD Positions Application Deadline 4 Aug 2026 - 23:59 (Europe/Madrid) Country Spain Type of Contract Permanent Job Status Full-time Hours Per Week 35 Offer Starting Date 1
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22 Jun 2026 Job Information Organisation/Company Fondazione Bruno Kessler Research Field Other Researcher Profile Other Profession Positions PhD Positions Application Deadline 20 Jul 2026 - 23:59
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an advantage: Deep learning or machine learning Structural bioinformatics Protein structure prediction and modelling Molecular simulations Membrane proteins or membrane biophysics Scientific software development
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PhD in Computational Imaging for High-Throughput Applications (1.0 fte) As a PhD student, you will be embedded in the research group Computational Imaging and Deep Learning (CIDL), part of
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engineering Engineering » Electrical engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 18 Sep 2026 - 23:59 (Europe/Brussels) Country Germany Type
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the subsequent years, you will combine these insights with predictive modelling and select, train, and validate process-based or deep-learning fire propagation models using a.o. detailed information about