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approach of data-driven membrane discovery that includes material space construction and exploration, candidate selection and verification, providing data for machine learning models to optimise membrane
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. Kinetic rates will be calculated on the fly from molecular dynamics simulations using machine learning potentials. This approach will provide guidelines to steer the formation process of zeolites by tuning
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in the field of cancer research The following are considered a plus: Experience with next generation sequencing data analysis or other genome-wide assays Experience with machine learning methods in
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hold an MSc degree in environmental science or ecology, with a proven expertise in data analysis, organizing and handling. Expertise in machine learning is a plus. A sound command of the English language
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in the field of cancer research The following are considered a plus: Experience with next generation sequencing data analysis or other genome-wide assays Experience with machine learning methods in
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described as intricate machines where proteins work together in a tightly coordinated fashion to produce essential cellular functions. Yet evolution challenges this picture. Proteins that are crucial for a
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of the identified structures via stereolithographic, 3D printing and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and
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, you will explore how data-driven models capturing the state-of-health and degradation can be integrated in the battery model. You will develop these machine learning-based proxies together with a
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10th October 2025 Languages English English English The Department of Electronic Systems has a vacancy for a PhD Candidate in Machine Learning & Signal Processing for Industrial Applications Apply
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an internationally recognized research team at LAAS-CNRS in Toulouse, focused on developing autonomous mobile machines that integrate perception, reasoning, learning, action, and reaction capabilities