Juan Rojo's Website

Group

My research group at the Department of Physics and Astronomy of the VU Amsterdam, embedded into the Nikhef Theory group brings together PhD candidates, postdoctoral researchers, and bachelor and master students working at the interface of Quantum Chromodynamics, effective field theories, high-energy neutrinos, and machine learning / artificial intelligence. Below are the current members of the group, followed by former members together with the topics they worked on. Prospective students and researchers interested in joining our group are encouraged to consult the Vacancies page or to contact me directly at j.rojo@vu.nl.

NNPDF Collaboration Meeting, Gargnano, September 2022.

Current group members

PhD candidates and postdoctoral researchers

  • Dr. Elie Hammou (postdoctoral researcher) — LHC neutrinos, effective field theories, AI for high-energy physics.
  • Peter Krack (PhD candidate) — LHC neutrinos, proton structure.
  • Jelle Koorn (PhD candidate) — LHC neutrinos, proton structure.
  • Kamil Laurent (PhD candidate) — effective field theories, proton structure.

Bachelor and master students

  • Paulina Hernandez Sainz (MSc) – Precise QCD event generation for neutrino astronomy.
  • Rowan van der Brink (MSc) — disentangling New Physics from QCD uncertainties at the LHC with SIMUnet.
  • Khoi Pham (MSc) — neural simulation-based inference with LHC neutrinos at FASER.
  • Wopke Telman (MSc) — effective field theories for New Physics at the HL-LHC and future colliders.
from left to right: together with some current and former group members at the 8th FPF Workshop (CERN, January 2025), the NNPDF Collaboration meeting (Morimondo, September 2025), and the SMEFiT Collaboration meeting (CERN, January 2026).

Former group members

PhD candidates and postdoctoral researchers

  • Tanjona Rabemananjara (postdoc, 2021–2025) — QCD resummation, machine learning for particle physics, and proton structure with hyperoptimised machine learning.
  • Tommaso Giani (PhD candidate and postdoc, 2018–2024) — nuclear parton distributions and SMEFT interpretations of LHC data.
  • Jaco ter Hoeve (PhD candidate, 2020–2024) — Fingerprinting New Physics with Effective Field Theories [PhD thesis].
  • Giacomo Magni (PhD candidate, 2020–2024, Cum Laude) — A High Resolution Imaging of the Collinear Substructure of the Proton [PhD thesis].
  • Rhorry Gauld (postdoc, 2018–2021, NWO Veni) — QCD, heavy-flavour production, and high-energy neutrino fluxes.
  • Jacob Ethier (postdoc, 2018–2021) — nuclear parton distributions, proton spin, and hadronization.
  • Rabah Abdul Khalek (PhD candidate, 2017–2021, Cum Laude) — Exploring the Substructure of Nucleons and Nuclei with Machine Learning. [PhD thesis]
  • Emma Slade (PhD candidate @ Oxford, 2016–2019) — precision fits at the LHC and beyond [PhD thesis].
  • Emanuele Roberto Nocera (PhD candidate 2010–2014; postdoc 2015–2017 @ Oxford and 2018–2020 @Nikhef) – Unbiased spin dependence of parton distribution functions [PhD thesis].
  • Valerio Bertone (postdoc, 2015–2017) — parton distributions and QCD evolution.
  • Nathan P. Hartland (postdoc, 2014–2017) — parton distributions and SMEFT global fits.
  • Francesco Giuli (PhD candidate, 2014–2018) — Drell-Yan production with the ATLAS detector at the LHC and impact on proton structure [PhD thesis].
  • Luca Rottoli (PhD candidate, 2014–2018) — precision QCD: proton structure and all-order resummations.
  • Marco Bonvini (postdoc, 2014–2016) — high-energy (small-x) resummation and proton structure.
  • Francesco Cerutti (PhD candidate, 2009–2013) — neural-network parton distributions for the LHC.

Bachelor and master students

  • Roan van Brussel (BSc, 2024) — fingerprinting New Physics at the FCC-ee with the SMEFT.
  • Thomas Spreen (BSc, 2024) — event generators for LHC neutrino experiments.
  • Wouter Prommel (BSc, 2024) — neutrino scattering cross-sections at high energies.
  • Gijs van Seeventer (MSc, 2024) — proton structure analyses with normalising flows.
  • Jukka John (MSc, 2024–2025) — a data-driven determination of the forward neutrino fluxes at the LHC from machine learning.
  • Valentina Schütze Sanchez (MSc, 2023–2024) — the LHC as a neutrino collider.
  • Eva Groenendijk (MSc, 2023–2024) — Predictions for High-Energy Neutrino Scattering at the Large Hadron Collider (thesis). Currently a PhD candidate at the University of Milan under the supervision of Prof. Stefano Forte.
  • Toon Hasenack (MSc, 2023–2024) — proton spin structure with machine learning.
  • Anezka Bos (BSc, 2023) — polarised proton structure from machine learning.
  • Wolf Gautier (BSc, 2023) — optimal observables for effective field theory interpretations.
  • Philip Fredriksz (MSc, 2023, with S. Conesa-Boj) — deep-learning denoising of high-resolution transmission electron microscopy images.
  • Stijn van der Lippe (MSc, 2022–2023, with S. Conesa-Boj) — automation of electron energy-gain spectroscopy data analysis.
  • Maaike Bakker (MSc, 2022–2023) — SMEFT projections for future high-energy colliders.
  • Steven Niedenzu (MSc, 2022–2023) — neutrino interactions and proton structure at the Forward Physics Facility.
  • Adrianne Schaus (MSc, 2022–2023) — machine-learning determination of proton structure with lattice QCD.
  • Pim Herbschleb (MSc, 2022–2023) — a SMEFT global analysis with flavour and low-energy data.
  • Josep Sola (MSc, 2022–2023) — probing small-x QCD with TeV neutrinos at the Forward Physics Facility.
  • Nusch Mortazavi (BSc, 2022) — machine-learning determination of neutrino-nucleus cross-sections.
  • Borak Apak (MSc, 2021–2022, with S. Conesa-Boj) — disentangling strain in nanomaterials with machine learning.
  • Charlie Bender (MSc, 2021–2022) — combined EFT interpretation of flavour and high-pT observables at the LHC.
  • Isabel Postmes (MSc, 2021, with S. Conesa-Boj) — automated pattern recognition for EELS spectral images.
  • Jochem Bakker (BSc, 2021) — SMEFT constraints from vector-boson-fusion processes.
  • Duncan Pelan (BSc, 2021) — heavy Majorana neutrino mass constraints in the Type-I seesaw from an effective field theory.
  • Laurien Roest (MSc, 2020, with S. Conesa-Boj) — machine learning for electron energy-loss spectroscopy.
  • Gijs van Weelden (MSc, 2019–2020) — perturbative QCD calculations for heavy-quark hadrons in cosmic-ray collisions.
  • Ferran Faura Iglesias (MSc, 2019–2020) — impact of the NOMAD data on the strangeness content of the proton.
  • Samuel van Beek (MSc, 2018–2019) — stress-testing the Standard Model at the high-energy frontier with Bayesian inference.
  • Marco Bout (BSc, 2018) — three-dimensional imaging of the proton with artificial neural networks.
  • Sanne Vergouwen (BSc, 2018) — constraining the SMEFT with top-quark measurements.
  • Paul Gorris (BSc, 2018) — Z-boson production in Pb-p collisions at 5.02 TeV.
  • Manuel Wierda (MSc, 2017–2018) — nuclear parton distributions from advanced machine-learning techniques.
  • Emanuel Hoogeveen (BSc, 2017) — Higgs pair production at a 100 TeV hadron collider.
  • Daniel Shipley (MSc, 2016) — searches for New Physics at the LHC using ratios of cross-sections.
  • Miriam Kuenzel (MSc, 2016) — New Physics searches at the LHC with Higgs pair production.
  • Carmen Gigliotti (MSc, 2011) — the impact of new theory in a global PDF analysis using Bayesian inference.
  • Giulia Calzolaio (MSc, 2011) — the impact of high-energy resummation on parton distributions and LHC phenomenology.
  • Marco Zaro (MSc, 2009) — kt-factorization in inclusive jet production.
  • Simone Lionetti (BSc, 2010) — dataset dependence of alpha-s determinations in global PDF analysis.
  • Alice Donati (BSc, 2009) — statistical aspects of the NNPDF determination of parton distributions.
  • Elisa Mariani (BSc, 2009) — determination of the QCD strong coupling constant in the NNPDF approach.