The summer internship positions 2026 will partly financed from the budget of the mayor of Graz, Elke Kahr, as well from the IWF budget.

Project: Cloud formation on Venus: A novel model approach to resolve the conundrum of the Venusian clouds

Supervisor: Dr. Manuel Scherf

Venus, the hellish twin of our home planet Earth, is covered by thick clouds mostly consisting of droplets that contain a mix of sulfuric acid, water and a wide, partially unknown component of dust particles that originate either from Venus’ surface, volcanic degassing or an influx of interplanetary dust particles and micrometeorites. The origin, physical and chemical behaviour of the clouds, however, are still mostly a conundrum and are accompanied by various unresolved scientific problems. Among others, these include (i) the disputed detection of phosphine, (ii) the unknown UV absorber in Venus’ clouds, (iii) a strong depletion of SO2 above the clouds, (iv) an unexpected increase in the deuterium to hydrogen ratio in the mesosphere, and (v) the composition and size-distribution of the cloud particles and aerosols themselves. This student internship aims at better understanding Venus’ clouds by investigating the origin, behaviour, distribution and chemical composition of aerosols in Venus’ atmosphere with DiffuDrift and GGchem, which will help in resolving above mentioned scientific conundrums.

Necessary knowledge: Master or Bachelor student in physics or space science; Interest in atmospheric physics and chemistry; Basic knowledge of Linux, Python (and interest in Fortran)

Recommended reading sources:

• Rimmer et al., 2021 (https://iopscience.iop.org/article/10.3847/PSJ/ac0156)
• Karyu et al., 2024 (https://iopscience.iop.org/article/10.3847/PSJ/ad25f3)
• Titov et al., 2018 (https://link.springer.com/article/10.1007/s11214-018-0552-z)
• Woitke, Scherf et al., 2025 (https://iopscience.iop.org/article/10.3847/PSJ/ae00bf)

Project: MMS Data Exploration & Energy Budget Analysis

Supervisor: Dr. Aditi Upadhyay and Dr. Zoltán Vörös

Earth’s magnetosheath, the turbulent, shocked solar wind region bounded by the bow shock and the magnetopause, is a unique natural laboratory for studying collisionless plasma turbulence across a wide range of spatial and temporal scales. Understanding how energy cascades from large magnetohydrodynamic (MHD) scales down to electron kinetic scales, and how it is locally converted and transported by coherent structures, is a central open question in space plasma physics. NASA’s Magnetospheric Multiscale (MMS) mission, flying four identical spacecraft in a tunable tetrahedral formation, provides the highest-resolution plasma and electromagnetic field measurements ever made in this region. This project introduces the student to the MMS mission and its unique data products, develops the physical and mathematical tools needed to compute plasma moments and energy terms from particle distribution functions, and places these in the context of multi- versus single-spacecraft analysis. The culminating step is to apply the acquired skills to compute local energy proxy (LEP) terms and contribute processed, labelled data intervals to the FWF project’s growing magnetosheath database.

Necessary knowledge: 

• Physics: Classical electrodynamics (Maxwell’s equations, Lorentz force); plasma physics fundamentals (frozen-in theorem, MHD); space physics context (magnetosphere structure, bow shock, magnetosheath) -- MSc level
• Mathematics: Vector calculus (div, curl, gradient); linear algebra (tensors, eigenvalue decomposition for MVA) -- BSc/MSc level
• Programming: Python (numpy, matplotlib, scipy); Jupyter notebooks -- MSc level
• Data Handling: Multi-dimensional arrays; pyspedas library introduced during the project -- No prior experience required

Recommended reading sources:

• Hadid et al., 2018 (https://doi.org/10.1103/PhysRevLett.120.055102)
• Paschmann & Daly (Eds.), 1998 (https://www.issibern.ch/PDF-Files/analysis_methods_1_1a.pdf)
• Burch et al., 2016 (https://doi.org/10.1126/science.aaf2939)

Project: Magnetopause Response to Interplanetary Magnetic Field Orientation Changes

Supervisor: Dr. Adriana Settino and Dr. Daniel Schmid

The Earth's magnetopause is a dynamic boundary separating the shocked solar wind plasma from the magnetosphere. The dominant coupling processes at this boundary are strongly controlled by the interplanetary magnetic field (IMF) orientation. For instance, southward IMF drives dayside magnetic reconnection, while northward IMF favors the Kelvin–Helmholtz instability. Both processes facilitate solar wind plasma entry into the magnetosphere.
The solar wind magnetic field is, however, highly variable in both magnitude and orientation. Despite extensive study of magnetopause dynamics under prolonged periods of quasi-steady IMF conditions, the transitional periods — when the IMF changes orientation — remain comparatively poorly understood. This project addresses two fundamental questions regarding this transition:

• How does the magnetopause respond to a IMF turning?
• On what timescale does the magnetopause readjust to the new IMF configuration?

Answering these questions is essential for advancing our understanding of solar wind–magnetosphere coupling under realistic, time-varying solar wind conditions and critical to understand the energy input into the magnetosphere.

Necessary knowledge: Plasma Physics knowledge would be beneficial. Programming skills in Python or Matlab are required.

Recommended reading sources:

• Paschmann et al., 2018 (https://doi.org/10.1002/2017JA025121)
• Burch et al., 2016 (https://doi.org/10.1002/2016GL069787)
• Samsonov et al., 2017 (https://doi.org/10.1002/2017GL075020)
• Samsonov et al., 2024 (https://doi.org/10.1029/2023JA032378)

Project: Neural-RT: A Neural Emulator for Radiative Transfer for Exoplanet Atmospheres

Supervisor: Dr. Amit Reza

Present and future space missions enable comparative studies of the weather and climates of ensembles of exoplanets orbiting different stars within our galaxy, where the host-star population and the chemistry-determining element abundances change. Exoplanet atmospheres are complex physio-chemical systems where cloud and gas chemistry are in close feedback loops with the local thermo- and hydrodynamics, which in turn are affected by the external radiation field of the host star and the local galactic metallicity. Complex models have been developed that are highly computationally intensive, including three major components of exoplanet atmosphere characterization: the 3D Global Circulation Model (GCM), Cloud modeling, and Radiative Transfer. This work aims to develop a Machine Learning (ML)-driven data interpretation pipeline as a fast, robust, and reliable alternative to classical components, in particular to develop a neural network (NN) emulator to accelerate the radiative transfer (RT) component, integrating with 3D GCM and cloud modelling.

Necessary knowledge: Knowledge in one or more of the following topics will be helpful: Exoplanet Atmospheric Modelling, Astrophysics, Atmospheric Science, and Machine Learning.

Recommended reading sources:

• Carone et al., 2025 (https://doi.org/10.48550/arXiv.2511.01548)
• Tahseen et al., 2024 (https://doi.org/10.1093/mnras/stae2461)
• Malsky et al., 2025 (https://doi.org/10.48550/arXiv.2510.27050)
• Plaschzug et al., 2025 (https://doi.org/10.1051/0004-6361/202555631)

On 19 August 2025, we had the pleasure of inviting this year's summer interns to a joint welcome event. Before each intern was given the opportunity to briefly present their project, there were a few icebreaker games. The event was rounded off with a round of introductions by the various research groups at the IWF. Strengthened with ice cream and lots of new ideas, our interns were then able to take a look at the IWF Astrolab at the end.

We plan such a welcome event also for summer 2026.