Open Positions

The Extreme Light Infrastructure – Nuclear Physics (ELI-NP) is a premier European large-scale research facility located near Bucharest, Romania, and part of a major international initiative in high-power laser science and advanced nuclear physics. A central research focus at ELI-NP is the development of innovative high-energy gamma-ray sources based on inverse Compton scattering, enabling a new class of precision experiments in nuclear and fundamental physics.

Building on extensive experience with the Munich Compact Light Source — a globally recognized inverse Compton source for high-brilliance X-ray radiation — this postdoctoral research project aims at the advanced development, experimental implementation, and performance optimization of key components of an inverse Compton gamma-ray source in close collaboration with ELI-NP. The project encompasses the design and realization of high-power optical resonators in the green wavelength regime, comprehensive experimental characterization, and dedicated measurement campaigns at the ELI-NP facility in Bucharest.

The position offers the opportunity to conduct independent, high-impact research at the interface of laser physics, accelerator science, and high-energy photon sources, while contributing directly to the advancement of a world-class international research infrastructure.

A strong background in laser physics, accelerator physics, X-ray or photon science is highly desirable.

For further information, please contact: franz.pfeiffer(at)tum.de

The Munich Compact Light Source (MuCLS) is a laboratory-scale synchrotron radiation facility located at the Munich Institute of Biomedical Engineering (MIBE) in Garching and operated by the Chair of Biomedical Physics. Its X-ray source uses the principle of inverse Compton scattering to produce a partially coherent, quasi-monochromatic and high-flux X-ray beam [1].

Among these techniques, propagation-based phase-contrast imaging (PBI) is a very versatile single-shot phase-sensitive X-ray imaging method. On the one hand, this enables studying dynamic processes in specimens of biomedical interest, such as in vivo small-animal X-ray imaging of airways or lungs [2]. On the other hand, rapid micro-CTs of centimeter-scale samples at resolutions better than 10 μm become feasible.

The current PBI framework at the MuCLS employs the standard Paganin single-material single-distance phase retrieval. Within the framework of this thesis, more sophisticated phase-retrieval techniques, e.g. employing convolutional neuronal networks or the Fokker-Plank equation, will be developed and implemented. Furthermore, the duration of the micro-CT scans should be reduced by exploiting (both classical and AI-based) methods for denoising and sparse sampling. The improved PBI framework will be used to measure biomedical specimens, e.g., cochleas.

Character of the thesis: theory, programming & data processing (60%), experimental (40%)

Experience in X-ray or phase-contrast imaging as well as Python programming is desirable.

For further information, please contact Prof. Franz Pfeiffer (franz.pfeiffer(at)tum.de).

[1] Günther et al. 2020. https://dx.doi.org/10.1107/s1600577520008309
[2] Gradl et al. 2018. https://dx.doi.org/10.1038/s41598-018-24763-8

The Munich Compact Light Source (MuCLS) is a laboratory-scale synchrotron radiation facility located at the Munich Institute of Biomedical Engineering (MIBE) in Garching and operated by the Chair of Biomedical Physics. Its X-ray source uses the principle of inverse Compton scattering to produce a partially coherent, quasi-monochromatic and high-flux X-ray beam [1].

X-ray absorption spectroscopy (XAS) provides element-specific information about the chemical surroundings of an atom and, therefore, has a wide range of applications in different research fields. The XAS setup implemented at MuCLS [2] has successfully been applied in different catalysis research projects in collaboration with the TUM Department of Chemistry [3].

This PhD project will focus on further developing the energy-dispersive XAS setup at MuCLS and its applications. Currently, the setup employs a flat crystal in Laue geometry to generate a spatial energy gradient (see [1] for details). This geometry also results in a polychromatic line focus, which in the past had been inaccessible for experiments due to geometrical constraints. With a recent major remodeling of the experimental area of the MuCLS, these constraints have been removed. The PhD project is expected to make use of this increased flexibility by designing, implementing, and conducting experiments with the polychromatic focus. Furthermore, dynamical bending of the crystal shall be investigated to control both the bandwidth and the focusing of the transmitted beam. Exploration of alternative setup geometries (through simulations and experiments) and combination with other techniques like, e.g., X-ray fluorescence spectroscopy, is desirable.

Character of the thesis: simulation & data processing (50%), experimental (including setup design) (50%).

Experience in Python programming and CAD is desirable. Previous experience with XAS is considered an asset but not a requirement.

For further information, please contact Prof. Franz Pfeiffer (franz.pfeiffer(at)tum.de).

[1] B. Günther et al. 2020. https://doi.org/10.1107/S1600577520008309
[2] J. Huang et al. 2020. https://doi.org/10.1038/s41598-020-65225-4
[3] J. Huang et al. 2021. https://doi.org/10.1039/D1JA00274K

The Extreme Light Infrastructure – Nuclear Physics (ELI-NP) is a leading European large-scale research facility located near Bucharest, Romania, and part of one of the most ambitious international initiatives in high-power laser science and nuclear physics. One of ELI-NP’s key research directions is the development of novel high-energy gamma-ray sources based on inverse Compton scattering for cutting-edge nuclear physics experiments.

Building on our long-standing expertise with the Munich Compact Light Source — a world-leading inverse Compton source for brilliant X-ray radiation — this PhD project focuses on the development and experimental realization of the core components of an inverse Compton gamma source in close collaboration with ELI-NP. The work includes the design, setup, optimization, and detailed characterization of a high-performance optical resonator operating in the green wavelength range, as well as extensive experimental campaigns at the ELI-NP research facility in Bucharest.

This doctoral project offers a unique opportunity to work at the forefront of modern laser physics and accelerator-based photon sources, contributing directly to an internationally visible flagship research infrastructure.

Background knowledge in laser physics, X-ray science, or accelerator physics is advantageous but not mandatory.

For further information, please contact franz.pfeiffer(at)tum.de

X-ray absorption spectroscopy (XAS) provides element-specific information about the chemical surroundings of an atom and, therefore, has a wide range of applications in different research fields.

Within this project, the energy-dispersive XAS setup at the MuCLS, our inhouse compact synchrotron, is re-commissioned after the upgrade of its experimental area and the data acquired with this setup is going to be processed and analysed.

Data analysis will be performed with support by an experienced synchrotron XAS team. Since spectroscopy data taken at the MuCLS and at energy-dispersive XAS synchrotron beamlines are processed the same, data already acquired at this beamline will be analysed within the project as well.

Character of the thesis: experimental (25%), data analysis & processing (75%),

Experience in Python programming is desirable.

For further information, please contact Dr. Martin Dierolf (martin.dierolf(at)tum.de), Dr. Benedikt Günther (benedikt.guenther(at)tum.de), or Prof. Franz Pfeiffer (franz.pfeiffer(at)tum.de).

In medical imaging, these detectors can provide spectral image information in addition to classic attenuation-based X-ray images. This information can be used to calculate material-specific images. Applications range from radiography involving bone and soft tissue, to iodine images in contrast-enhanced mammography.

However, spectral imaging with multi-layer flat detectors presents several challenges. For example, different scintillator layer thicknesses can affect image quality, the modulation transfer function (MTF) and the detective quantum efficiency (DQE), as well as spectral separation. 

The individual detector layers also typically need to be aligned with each other. The interpolation required for this can lead to a deterioration in the MTF of the individual layers.

Material-decomposed images contain noise from all detector layers, resulting in a significantly poorer contrast-to-noise ratio (CNR) than attenuation-based images.

The aim of this master's thesis is therefore to investigate a multi-layer flat detector from a systems theory perspective and validate the resulting models using a two-layer detector prototype. 

This work is being carried out in collaboration with industrial partner Siemens Healthineers in Forchheim.

Character of the thesis: Image processing (60%), experimental (40%).

Experience in Python programming is desirable.

For further information, please contact Prof. Franz Pfeiffer (franz.pfeiffer(at)tum.de).

This project seeks to model comprehensive financial market dynamics through the innovative integration of physics-inspired functions and artificial intelligence techniques. Long-term market growth is represented using an exponential function with linear–quadratic parameters, enabling the capture of both sustained trends and acceleration or deceleration effects over time. Sudden market downturns are realistically depicted through negative exponential signal pulses, incorporating parameters such as decay time and bonding potential to reflect market resilience. In addition, speculative phases are modelled using specialised analytical functions designed to identify and predict the emergence of market bubbles.Long-term cyclical fluctuations are analyzed and modeled using techniques such as Fourier Series or wavelet decomposition to capture underlying periodicities and trends.

The methodology further involves separating the low frequency from noise using Empirical Mode Decomposition (EMD). The overall framework is validated through extensive backtesting on historical financial data.

Strong proficiency in Python is essential for this project, along with a strong interest in machine learning—particularly CNNs—and financial markets.

For more information, please contact: Prof. Franz Pfeiffer (franz.pfeiffer(at)tum.de)

This project aims to model and predict comprehensive financial market dynamics through the systematic application of Hidden Markov Models (HMMs). The approach represents market behaviour as a sequence of latent states corresponding to different market regimes, such as growth phases, high-volatility periods, speculative bubbles, and crash scenarios. Transitions between these hidden states are governed by probabilistic rules, enabling the realistic capture of regime shifts and temporal dependencies in financial time series.

Observable market indicators, including price movements (to start with), volatility measures, and trading volumes (later), are used to infer the underlying market states and to estimate transition probabilities. By learning these patterns from historical data, the model can identify prevailing market conditions and forecast likely future developments. The methodology allows for both short-term dynamics and long-term structural changes to be incorporated within a unified probabilistic framework.

The predictive performance is evaluated through rigorous backtesting using historical market data, assessing the model’s ability to detect regime changes and anticipate major market movements.

Essential requirements include solid Python programming skills, along with a strong interest in statistical modelling, machine learning, and financial markets.

For more information, please contact: franz.pfeiffer(at)tum.de

This project applies advanced deep learning techniques, specifically one-dimensional Convolutional Neural Networks, to model and forecast complex financial market dynamics. Financial time series, including price movements, returns, volatility indicators, and trading volumes, are interpreted as sequential signals, allowing the network to automatically extract meaningful temporal features to predict future probabilities for price changes, incl. Probabilities for abnormal market behaviour and sudden crash events. 

Through stacked convolutional layers with diverse kernel sizes, the model learns to capture both short-term market fluctuations and longer-term structural patterns. These learned features support accurate trend prediction, identification of regime shifts, and detection of critical market events such as crashes or speculative surges. The network architecture is optimised to efficiently process large financial datasets while maintaining strong generalisation capabilities.

The workflow encompasses data preparation, network training and tuning, and comprehensive validation using historical market data. Predictive performance is assessed through rigorous backtesting and relevant quantitative metrics.

Proficiency in Python is required, along with a strong interest in deep learning methods, time series modelling, and financial markets.

For more information, please contact: franz.pfeiffer(at)tum.de