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• Paola AYALA - Gas sensing with carbon nanotubes as impurity-free hybrid systems

  The quest for advanced gas sensors has led researchers to explore single-walled carbon nanotubes (SWCNTs) as promising candidates. These molecular-scale tubes excel with their properties, such their remarkable surface-to-volume ratio and electrical conductivity. Yet, despite their potential, creating next-generation sensors faces significant challenges: we need devices that can detect gases with high precision, distinguish between different molecules effectively, respond quickly, and reset efficiently. This project addresses a critical gap in the field: understanding why SWCNT-based sensors still struggle with reliability and recovery. To tackle this, we will develop a specialized testing chamber that will allow us to study films of nanotube hybrids under precisely controlled atmospheric conditions. This chamber´s basic unit is expected as a cell compatible with both analytical spectroscopy and scanning probe microscopy, ensuring consistent gas exposure across the different measurement techniques.

  By enabling standardized testing conditions, this work aims to uncover the root causes and issues that still hinder the widespread use of nanotube-based sensors.

   

  Information related to the research in our group can be found here: https:\\ths.univie.ac.at
• Paola AYALA - Using Ultraviolet Photoemission Spectroscopy to understand the electronic properties of doped carbon nanotubes

  The growing interest in alternative materials to traditional semiconductors has introduced new challenges in the research on single-walled carbon nanotubes (SWCNTs). Beyond simply producing batches with controlled metallic or semiconducting properties, researchers now seek a deeper understanding of how to precisely engineer the electronic characteristics of these nanomaterials. In bulk semiconductor technology, doping at the parts-per-million level is a well-established approach for tuning electronic properties. However, when it comes to semiconducting SWCNTs, the story is more complex. When heteroatoms like boron (B) substitute for carbon atoms in the nanotube lattice, the doping levels required to induce a rigid shift in the electronic band structure are not yet fully understood.  This project aims to address this knowledge gap. The goal is to develop methods for producing highly stable, B-doped SWCNTs with ultra-low doping levels. By leveraging advanced spectroscopy techniques (especially Ultraviolet Photoemission Spectroscopy) , the research will explore the extent to which such precisely doped nanotubes exhibit the desired rigid band shift in the valence band - a fundamental characteristic that is well-characterized in bulk semiconductors but remains elusive for these nanoscale materials.

  Solving this challenge holds great promise for unlocking the full potential of SWCNTs as alternatives to traditional semiconductors in a wide range of electronic and optoelectronic applications.

   

  Information related to the research in our group can be found here: https:\\ths.univie.ac.at
• Andrii CHUMAK - Advancements in Experimental Inverse-Design Magnonics

  Experimental

  
  Field Overview: Magnonics focuses on studying and manipulating magnons—quantized spin waves that carry and process information with high energy efficiency. Inverse design in magnonics defines target functionalities first, followed by engineering optimized structures through computational algorithms. This project will further develop an existing magnonic processor, previously employed for RF functionalities and logic gates, to explore its potential in unconventional computing applications.

   

  Target Area: MSc students with a solid foundation in condensed matter physics, ideally with experience in magnonics.
  
  Website: https://nanomag.univie.ac.at/research/inverse-design/
• Andrii CHUMAK - Optical Detection of Spin-Waves at Cryogenic Temperatures

  Experimental

  
  Field Overview: The field of magnonics deals with spin waves, and their quanta called magnons, as the eigenexcitations of the collective electron spin subsystem of magnetically ordered media. One of the main experimental tools to detect spin-waves is Brillouin Light Scattering Spectroscopy (BLS), where we measure the frequency shift after the inelastic scattering of photons with magnons. In this project, we will implement this widely used technique into a dilution refrigerator setup, allowing to optically probe spin-waves at decreasing temperatures. The successful realization of this experimental setup would offer exciting opportunities for investigating the quantum character of magnons.
  
  

  Target Area: MSc students with a solid foundation in condensed matter physics and interest for experimental optics.
  
  Websites:

  https://nanomag.univie.ac.at/research/hybrid-quantum-systems/
  https://nanomag.univie.ac.at/research/quantum-magnonics/
• Andrii CHUMAK: Gate-defined quantum dots in bilayer graphene

  Field Overview: Quantum dots in bilayer graphene are an exciting new platform for quantum information technologies, where information is encoded in the spin or valley degree of freedom of a single electron. At the same time, these devices serve as excellent spin-, valley-, time- and energy-resolved sensors that can be operated in the quantum limit. In this project, you will join us in pioneering this new and exciting field. Depending on your interests, you will gain experience in the fabrication of state-of-the-art quantum devices, performing condensed matter experiments at cryogenic temperatures and learn about quantum physics, electronic transport, quantum dots and 2D-materials. 

  Experimental.

  You will be working with Luca Banszerus

  Target Area: We are seeking motivated MSc students with a background in condensed matter physics or quantum physics. Prior experimental experience in these areas is beneficial but not required.
• Cesare FRANCHINI - Machine Learning for Materials Physics

  Theoretical/computational

  
  Machine learning is transforming materials science by enabling rapid, accurate analysis of complex properties, predicting new materials, and optimizing fabrication processes. Its applications extend to enhanced discovery, design, and performance predictions, offering unprecedented insights at atomic level, enabling the exploration at large length and time scales and helping in deciphering quantum many-body effects in materials.

  In this project, the student will first explore various machine learning techniques and then select one to apply to a specific problem in materials physics.

  Available ML architectures: ML inter-atomic potentials, Computer Vision, Normalizing Flows, Bayesian optimization, Supervised Learning techniques

  Target area: 2nd/3rd year bachelor, MSc students
• Thomas JUFFMANN: Super-resolution microscopy based on elastic scattering

  Experimental

  Microscopy techniques based on elastic light scattering are powerful tools for studying nanoscale objects. Detecting the scattered light of metal nanoparticles or single proteins led to applications like the ultrafast tracking of dynamics on cell walls or mass photometry. Unlike fluorescence microscopy, elastic scattering offers high scattering rates, low phototoxicity, and avoids bleaching. However, it lacks specificity and the internal-level structure supporting super-resolution microscopy. Consequently, the spatial resolution is limited by the excitation wavelength. You will join a team that aims to overcome this limit.

  Target area: Physics students in the last Bachelor year or at the beginning of their Master studies

   

  https://imaging.univie.ac.at/
• Toma SUSI: Simulating transmission electron microscopy of ionic materials

  Theoretical project

  Target area: MSc students (4th year Bachelor students are also welocme to apply)

  Description: Transmission electron microscopy (TEM) image simulations are often based on independent atoms, which are a poor description for ionic materials with significant charge transfer. Although these can be treated with density functional theory, it would be beneficial to parametrize ionic potentials to enable easier and faster simulations, especially for tilted specimens. Building on prior work, the project is to test the implementation of this in our open-source code abTEM, and thus experience with Python is necessary and with TEM desirable.
• Philip WALTHER - Quantum Information Science and Quantum Computation

  Please note:

  • The following projects are not VDSP Summer Internship projects (different funding)
  • Duration: minimum 3 months
  • Application only possible on MSc level

   

  Project 1

  We are looking for an intern to work on quantum machine learning on photonic platforms. In this research area we design and implement classical machine learning algorithms on quantum hardware. The goal is, on one side, to understand the potentialities of quantum computing and, on the other, to look for potential enhancements in the performance of the chosen algorithms. From the experimental point of view, we use single photon sources based on Spontaneous Parametric Down-Conversion and integrated photonic circuits. In this internship, you will assist in building a new experiment and in the data collection and analysis.

   

  Project 2

  We are looking for an intern to work at the intersection of nonlinear optics, nanophotonics and quantum optics.  In this line of research, we use nanophotonic structures in ultra-thin materials to enhance the nonlinear efficiency of quantum processes such as spontaneous parametric downconversion.  In this internship, you will assist in building a new experiment to collect and characterize entangled photons from a new class of nonlinear optical media.

   

  https://walther.univie.ac.at/