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Actuators, using the two main existing methodologies. Carry out the experimental implementation of plasma actuators in wind turbines. Where to apply Website https://seuelectronica.upc.edu/en/procedures
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Knowledge in the field of Machine Learning, including training, inference, and optimisation of transformer architectures. Knowledge in the field of ML security is desirable. Good Python skills, especially
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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development from spatial transcriptomics data. Activities : – design of a new mathematical method – monitoring and study of publications relevant to the field – programming/coding in Python (Pytorch
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experiment codebases (prefer Python). Apply causal inference and discovery frameworks to clinical questions. Translate proposed methods and frameworks into real-world clinical workflows. Contribute to grant
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. • Strong proficiency in Python and relevant libraries for data analysis and modelling (e.g., TensorFlow, Keras, Scikit-learn, Pandas); knowledge of R would be an asset. • Familiarity with geospatial data
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Engine. • Programming skills in Python or similar scientific computing environments, with experience in geospatial and machine learning libraries. SKILLS AND QUALITIES • Strong scientific rigor and
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the any of the following software: Matlab Python, other Preferred (not all): Knowledge and/or interest on control Experience in collaborative and international projects Experience/knowledge in HIL systems
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on cybersecurityl Knowledge and experience on advanced modelling Knowledge and experience with the any of the following software: Matlab Python, other Preferred (not all): Knowledge and/or interest on vulnerability
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August 2026 (or later), with the possibility of renewal depending on performance and available funding. To apply, please upload the following materials on the following postdoctoral application https