The following topics are offered for BSc, MSc and PhD theses. If you are interested, get in touch with me directly via email, providing the following information:
1. CV
2. grade transcript
3. which project you are interested in from the ones listed below
4. whether you satisfy the prerequisites
[Project #1] Detector development: characterisation of ultra-fast silicon timing sensors
Join an exciting hands-on project at the frontier of detector technology, where you’ll contribute to the development and characterization of ultra-fast silicon sensors intended for use in next-generation physics experiments.
Main Objectives:
* Measure the leakage current of advanced silicon sensors.
* Measure the time resolution of the sensors using a radioactive source.
* Design and implement a test setup with temperature and humidity monitoring, including shielding and environmental control.
* Implement real-time monitoring and data logging through oscilloscope control using Python scripts.
Skills & Competences Acquired:
* Deep understanding of how silicon sensors are used and characterized in modern physics.
* Insight into detector electronics, basic circuit design, and noise mitigation.
* Practical experience in instrumentation, automation, and data analysis.
* Familiarity with lab tools such as oscilloscopes, signal generators, and environmental sensors.
Prerequisites:
You should have at least followed the courses below:
i) Introduction to nuclear and particle physics
ii) Accelerators and detectors
Ideal for Students Who:
* Enjoy hands-on experimental work and problem-solving.
* Are curious about detector R&D and want to gain real lab experience.
* Are independent, proactive, and not afraid to try, fail, and improve.
* Want to contribute to research with real-world impact in high-energy or nuclear physics.
International Collaboration:
This project is carried out in collaboration with the University of Freiburg, offering potential for cross-border scientific exchange and exposure to a broader research community.
[Project #2] Development of novel benchmarks and analyses for Dark Matter searches at colliders
Contribute to the global effort to uncover the nature of Dark Matter by developing new search strategies at the Large Hadron Collider (LHC) at CERN.
In this project, you'll help design and implement novel benchmark models and/or analysis techniques aimed at improving the sensitivity of current and future LHC searches for Dark Matter particles. Your work will directly feed into ongoing efforts by the LHC Dark Matter Working Group, potentially influencing real experimental searches.
Main Objectives:
* Develop benchmarks for unexplored Dark Matter signatures at the LHC
* Optimize analysis strategies to maximize signal-to-background discrimination.
* Produce expected exclusion limits for LHC Run 3
Skills & Competences Acquired:
* In-depth knowledge of particle physics phenomenology, from theoretical models to experimental signatures.
* Practical experience with state-of-the-art Monte Carlo tools such as MadGraph, Pythia, and Rivet.
* Scripting and workflow automation using bash, Python, and C++, applied to real research problems.
* Familiarity with data analysis techniques used by LHC collaborations.
Prerequisites:
You should have at least followed the courses below:
i) Introduction to nuclear and particle physics
ii) Physics of Elementary Particles
Bonus: Prior experience with programming (especially in Python or C++) is helpful but not required—motivation and willingness to learn are more important.
Ideal for Students Who:
* Are interested in the interplay between theory and experiment in modern particle physics.
* Want to explore cutting-edge questions in Dark Matter and BSM physics.
* Enjoy coding and are excited about using computational tools in a research context.
* Are independent, analytical, and curious about how new physics signals could be detected in real collider data.
International Collaboration:
This project is carried out in the context of the LHC Dark Matter Working group and in collaboration with the Max Planck Institute in Munich.
[Project #3] Leveraging low-level detector information to improve b-jet identification and searches for new physics
Join the frontier of experimental particle physics by developing advanced techniques that use low-level detector information—such as inner-tracker hits—to improve the identification of b-jets and exotic signatures like emerging jets, potentially revealing signs of new physics at the Large Hadron Collider (LHC).
This project blends detector physics, machine learning, and BSM phenomenology into a hands-on research experience aimed at pushing the limits of LHC search strategies.
Main Objectives:
* Develop and optimize machine learning algorithms that utilize low-level detector inputs to enhance b-jet tagging and identification of non-standard jet signatures.
* Explore the feasibility and performance of hit-based ML algorithms at the trigger level.
* Quantify the impact of these approaches on expected exclusion limits for LHC Run 3 searches.
Skills & Competences Acquired:
* Understanding and application of machine learning techniques to real physics problems.
* Practical experience with scripting and automation using Python, C++, and bash.
* Exposure to data analysis methods used by LHC collaborations.
Prerequisites:
You should have at least followed the courses below:
i) Introduction to nuclear and particle physics
ii) Accelerators and Detectors
Important: basic knowledge of Linux and python is required.
Ideal for Students Who:
* Are excited by the intersection of machine learning and experimental physics.
* Want to contribute to real-world improvements in trigger strategies and jet tagging at the LHC.
* Enjoy programming, data analysis, and working with complex detector information.
* Are curious, self-driven, and have strong problem solving skills.
International Collaboration:
This project is carried out in the context of the ATLAS experiment at CERN and in collaboration with the University of Oregon.
[Project #4] Study potential interactions of the Higgs boson with Dark Energy
Recent theoretical models suggest that Dark Energy may be associated with light scalar particles—such as chameleons—that could interact with the Higgs boson. In this project, we investigate the interplay of the Higgs with chameleon particles and assess whether these particles could be detected on Earth.
This is an exploratory, semi-phenomenological project where students will analyze a frontier scenario connecting cosmology, particle physics, and collider data.
Main Objectives:
* Estimate the lifetime and decay length of the chameleons based on model parameters.
* Determine whether chameleons could reach Earth-based detectors (e.g., MATHUSLA, FASER) or decay in flight.
* Discuss the potential implications for collider experiments and indirect constraints from astrophysics or cosmology.
Skills & Competences Acquired:
* Deep understanding of Higgs physics and dark sector phenomenology.
* Hands-on experience with analytic calculations in particle physics
* Use of Python or Mathematica for symbolic and numerical computations.
* Exposure to how collider experiments interface with cosmological theories.
Prerequisites:
You should have succesfully ollowed the course Physics of Elementary Particles.
Important: familiarity with Mathematica is a bonus.
Ideal for Students Who:
* Are curious about fundamental questions connecting Dark Energy, particle physics, and the Higgs boson.
* Enjoy theoretical problem solving and using models to make testable predictions.
* Are independent thinkers, have good problem solving skills and are comfortable navigating a project that blends theory and phenomenology.
International Collaboration:
This project is carried out in collaboration with the University of Notthingham and the University of Glasgow.