Vacancies

Here we list possible job opportunities within my group, as well as available bachelor and master projects.

PhD studentships to start in Autumn 2020

We are looking for candidates for two PhD positions in the Nikhef Theory group. The successful candidates will focus on phenomenological interpretation of data from the LHC and other experiments, both within the Standard Model (determinations of the proton structure) and within the SMEFT. These projects have a strong computational component related to the development and application of new Machine Learning techniques.
Requirements. Applicants should have a Master degree (or equivalent) in physics and good knowledge of Quantum Field Theory and the Standard Model. Good software skills and programming experience are prerequisites for these positions.
Applications need to be submitted via the Nikhef vacancies page.
Conditions. The candidate will receive a 4-year contract. The gross monthly starting salary will be € 2.407,-, increasing to € 3.085,- in the fourth year. The conditions of employment of NWO-I are excellent and can be found on www.nwo-i.nl. The starting date of the appointments is in the autumn of 2020. 
As Theoretical Physics PhD students, the candidates will become members of the Dutch Research School of Theoretical Physics (DRSTP), offering a wide spectrum of activities.

Master projects UvA/VU Physics & Astronomy 2020-2021

More information on available MSc projects in my group here.

The Effective Field Theory Pathway to New Physics at the LHC

A promising framework to parametrise in a robust and model-independent way deviations from the Standard Model (SM) induced by new heavy particles is the Standard Model Effective Field Theory (SMEFT). In this formalism, beyond the SM effects are encapsulated in higher-dimensional operators constructed from SM fields respecting their symmetry properties. In this project, we aim to carry out a global analysis of the SMEFT from high-precision LHC data, including Higgs boson production, flavour observables, and low-energy measurements. This analysis will be carried out in the context of the recently developed SMEFiT approach [1] based on Machine Learning techniques to efficiently explore the complex theory parameter space. The ultimate goal is either to uncover glimpses of new particles or interactions at the LHC, or to derive the most stringent model-independent bounds to date on general theories of New Physics. Of particular interest are novel methods for charting the parameter space [2], the matching to UV-complete theories in explicit BSM scenarios [3], and the interplay between EFT-based model-independent searches for new physics and determinations of the proton structure from LHC data [4].

[1] https://arxiv.org/abs/1901.05965 [2] https://arxiv.org/abs/1906.05296 [3] https://arxiv.org/abs/1908.05588 [4] https://arxiv.org/abs/1905.05215

Charting the quark and gluon structure of protons and nuclei with Machine Learning

Deepening our knowledge of the partonic content of nucleons and nuclei [1] represents a central endeavour of modern high-energy and nuclear physics, with ramifications in related disciplines such as astroparticle physics. There are two main scientific drivers motivating these investigations of the partonic structure of hadrons. On the one hand, addressing fundamental open issues in our understanding in the strong interactions such as the origin of the nucleon mass, spin, and transverse structure; the presence of heavy quarks in the nucleon wave function; and the possible onset of novel gluon-dominated dynamical regimes. On the other hand, pinning down with the highest possible precision the substructure of nucleons and nuclei is a central component for theoretical predictions in a wide range of experiments, from proton and heavy ion collisions at the Large Hadron Collider to ultra-high energy neutrino interactions at neutrino telescopes. The goal of this project is to exploit Machine Learning and Artificial Intelligence tools [2,3] (neural networks trained by stochastic gradient descent) to pin down the quark and gluon substructure of protons and nuclei by using recent measurements from proton-proton and proton-lead collisions at the LHC. Topics of special interest are i) the strange content of protons and nuclei, ii) parton distributions at higher-orders in the QCD couplings for precision Higgs physics, iii) the interplay between jet, photon, and top quark production data to pin down the large-x gluon, and iv) charm quarks as a probe of gluon shadowing at small-x. The project also involves developing projects for the Electron-Ion Collider (EIC), a new lepton-nucleus experiment to start operations in the next years.

[1] https://arxiv.org/abs/1910.03408 [2] https://arxiv.org/abs/1904.00018 [3] https://arxiv.org/abs/1706.00428

Machine learning for Electron Microscopy for next-generation materials

Machine Learning tools developed and applied for particle physics hold great potential for applications in material science, in particular concerning faithful uncertainty estimation and model training for large parameter spaces. In this project, carried out in collaboration with the group of Dr. Sonia Conesa-Boj from the Kavli Institute Nanoscience Delft, , we will develop and deploy ML tools for data analysis in Electron Microscopy. We will focus on pinning down the properties of novel quantum materials such as topological insulators and van der Waals materials. Examples of possible applications include model-independent background subtraction in electron-energy loss spectroscopy, automatic classification of crystalline structures, and enhancing spatial and spectral resolution using convolutional networks.