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requirements: Candidates must hold, at the time of application, a Bachelor’s degree in Informatics Engineering or related fields. Candidates must also have knowledge in: i. Deep Learning and LLMs: practical
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technology management, or smart grids. Experience in development of mathematical meta-models, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno
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Intelligence (AI) algorithms, including Machine Learning (ML) and Deep Learning (DL) techniques, for advanced signal analysis. The work will focus on developing methodologies for the detection, extraction
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possible renewals, a cumulative period of four years of research fellowship intended for doctoral students. Preferential factors: Research experience in the areas of Bioinformatics and Machine Learning
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, with applications ranging from scientific research to medical imaging and marketing analysis. With the ever increasing amount of learning data, these algorithms face computational challenges
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learning-assisted computational pipeline for the automated detection of point defects in atomic-resolution scanning transmission electron microscopy (STEM) images. Using monolayer MoS₂ as a model system, the
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learning (“buddying”) Both roles will contribute to a developing cross-disciplinary Deep End research community Developing Skills & Expertise: Develop academic skills around research from design to delivery
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programming in C (on microcontrollers and embedded Linux); ii. Machine learning on the Edge. Priority will be given to candidates enrolled in a Master Program related to Embedded Systems or related fields
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throughout the entire doctoral programme. Together we will draw up a career plan that includes the skills and knowledge you will acquire An inclusive working environment with ambitious colleagues
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with an important social mission Careers advice and guidance throughout the entire doctoral programme. Together we will draw up a career plan that includes the skills and knowledge you will acquire