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time; human resources management; quality of life and well-being; b) Experience in analysing quantitative data (e.g. European databases, structural equation modelling) and qualitative data (interviews
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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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for the production of complex porous structures B.CAD modelling and CFD simulation of TPMS structures and other geometries applicable to chromatography. C.Production and physical, morphological and
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to attach when applying: Curriculum Vitae, duly updated, dated, and signed; Copy of academic degree certificates. If the possession of a degree is a requirement for the grant, candidates holding a degree
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curricular evaluation purposes, proven skills in programming, preferably Python; machine learning and artificial intelligence; language models and retrieval-augmented generation (RAG); ASR/TTS; diarisation
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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs
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) Development of workflows and methods enabling AI-powered decision assistants to support full human operators control under risk and model uncertainty, and considering human-AI co-learning.; 2) Develop
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technologies and their application in high-resolution visualization systems. Optical modelling and simulation using specialized software (e.g., Zemax, Matlab, Mathematica). Development and integration of optical
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for modelled signal characterisation with a wide range of applications that goes from very low Gravitational Wave signals to Gravimetry. Fellowship 5 – Investigation of the electro-optic properties in BaTiO3
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rare genetic variants. Development of a bioinformatic algorithm for variant interpretation. Validation of results in the clinical setting and development of a model report for communicating results