Master Projects Juan Rojo VU Amsterdam & Theory group, Nikhef for more information: https://juanrojo.com/vacancies/ https://inspirehep.net/authors/1019897 1
My research in a nutshell precision calculation Effective Theories and model-independent of LHC processes searches for new physics P Proton substructure from machine learning Astroparticle physics Machine Learning algorithms & neutrino telescopes for and beyond particle physics embedded in Nikhef’s Theory Group , close connection with experimental groups current group: 2 postdocs, 1 PhD student (2 more joining in Oct), several master students
The EFT pathway to New Physics Bottom-up approach to new physics beyond the Standard Model (BSM) Effective Field Theories parametrise the space of possible BSM theories in terms of higher-dimensional operators to be constrained from data
Proton and nuclear structure from machine learning ξ (2) nNNPDF1.0 Quark and gluon substructure of nucleons and 1 nuclei determine the initial-state of proton and g ( x , Q 0 , A ) = ξ (1) ξ (2) ξ (3) x heavy-ion collisions B g x − α g (1 − x ) β g ξ (3) 1 2 1 1 Σ ( x , Q 0 , A ) = ξ (1) Determine in a model-independent manner by ξ (3) ln 1/ x 2 2 x − α Σ (1 − x ) β Σ ξ (3) 2 means of deep learning techniques T 8 ( x , Q 0 , A ) = ξ (1) ξ (3) A 3 3 x − α T 8 (1 − x ) β T 8 ξ (3) (n)PDFs also required for ultra-high-energy 3 astrophysics ξ (2) 25
Machine Learning for Material Science Recent instrumentation progress in electron microscopy (EM) characterisation of quantum materials requires the deployment of machine learning algorithms for data interpretation Apply our HEP-based ML expertise to the EM analysis of nanomaterials in collaboration with researches at the Kavli Institute of Nanosience Delft
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