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, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple
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at least one deep-learning framework (PyTorch preferred).•A solid grounding in machine learning. Experience with representation learning, generative models, foundation models or multimodal integration is a
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machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in industrial furnaces
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19 Aug 2026 Job Information Organisation/Company KU LEUVEN Research Field Engineering » Electrical engineering Engineering » Electronic engineering Engineering » Computer engineering Computer
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the suitability and potential benefits of LLMs (and other machine learning models) in these tasks. These investigations include the feasibility, practicality and success evaluation of prototype implementations
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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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. 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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-order modelling, surrogate models, and machine-learning methods such as neural networks. Control design for flexible reactor operation. Develop advanced control strategies that enable safe and efficient
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. Knowledge of data analysis, optimization, and machine learning techniques is a plus. Knowledge of machine learning libraries (e.g., PyTorch or TensorFlow), SDR hardware (e.g., USRP) and software (e.g
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PhD in Computational Simulations of Turbulent Reaction Flows for Clean Energy and Sustainable Propul
machine-learning methods, you will analyze flame-turbulence interactions, pollutant formation, as well as unclosed terms relevant to LES modeling. The analysis involves the fluid dynamic as