-
, 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
-
the numerical modelling of thermal phenomena, with particular emphasis on solidification processes. d) Knowledge on machine learning methods or data-driven modelling approaches applied to materials science or
-
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
-
related scientific and technical activities; to monitor the work carried out within the scope of the projects under their responsibility; to collaborate in the development of training actions in the field
-
of the projects under his/her responsibility; collaborate in the development of training actions within the scope of the R&D methodology; monitor the research work carried out by the scholarship holders, research
-
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