Stellenangebote - Bachelorarbeiten

Das in Heidelberg entwickelte ³He-Atomstrahl-Spinecho-Spektrometer ermöglicht das Vermessen kleinster Energieänderungen (~100 peV) in der Wechselwirkung zwischen Atomen und Felder oder Materie. Das Experiment ist in seiner Form weltweit einzigartig und erforscht derzeit:

• Casimir Kräfte und Quantenreibung
• Nicht-Newtonische Gravitation
• Axion-Suche (Dark Matter Kandidat)
• Geometrische Phasen

Für diese spannenden und grundlegenden physikalischen Fragen können wir Hilfe gebrauchen, sowohl am Experiment, als auch bei der Theorie. Interesse mit zu machen? Dann meldet euch bei:

• P.D. Maarten DeKieviet (maarten.dekieviet@physik.uni-heidelberg.de)

The Paul Scherrer Institute (PSI, Switzerland) operates the highest intensity proton accelerator (HIPA) in the world. This facility also provides high intensity muons beams where the muons are produced in pion decays. An upgrade of the accelerator is planned for 2026 with the goal to increase the muon beam intensity by almost two orders of magnitude, thus providing 10^10 muons per second. This upgrade will dramatically increase the discvery potential for rare physics processes like mu->eee (Mu3e Phase II) and mu-> e gamma which are also called the "golden" muon decay channels. Simulation and design studies need to be performed to reduce background and optimise the sensitivity.  

Instead of having special mathematics for all the different fields of physics, Geometric Algebra (GA) supplies a unified and unifying mathematical language for the whole of physics. It not only allows for a geometric interpretation of the constituent elements, it uncovers hidden connections between the otherwise seemingly unrelated mathematical descriptions. „Why hasn‘t anyone told me that before?“ is a regularly heard, awing reaction of students being exposed to this language for the first time. We are currently searching for discrepancies with and extensions of the regular mathematical approach, both theoretically and experimentally. These are exciting times, come and join us!

Our group explores the feasibility of applying modern machine learning algorithms such as Graph Neural
Networks (GNN) deployed on FPGAs for online track reconstruction within the ATLAS online server farm
for the High-Luminosity LHC (HL-HLC) upgrade. GNNs are a powerful class of geometric deep learning
methods for modelling spatial dependencies via message passing over graphs. They are well-suited for
track reconstruction tasks by learning on an expressive structured graph representation of hit data. A
considerable speed-up over CPU-based execution is possible on FPGAs.
We can offer several topics for theses:

1. focusing on performance simulation aspects and model optimizations
2. limiting the input data to e.g. the pixel detector only and studying the performance if the GNN only delivers track seeds or even only hit triplets
3. focusing on model translation and hardware deployment
4. studying the robustness of the models with respect to detector deformations
    

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