
Postdoc in theoretical condensed matter physics at Uni-A. Main research areas: Quantum computing, spin qubits, and quantum optimal control. Also HPC.
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University of Augsburg
Data Scientist
June 27, 2026 – Present
ParameterRun
March 25, 2026 – Present
Function to simplify the implementation of parallel runs of a given fucntions with no dependencies between runs
View Projectgeodesiq-control
March 10, 2026 – Present
Development of geodesiq Python package for optimal pulse control.
View ProjectLong_range_QQD
July 22, 2025 – July 22, 2025
Jupyter notebooks to reproduce the figures shown in the manuscript titled: Long-range spin transport in asymmetric quadruple quantum dot configurations
View ProjectAlpha-beta-hypergeo
April 8, 2025 – April 8, 2025
Jupyter notebooks and scripts to reproduce all the figures in the article: Unifying adiabatic state-transfer protocols with (α, β)-hypergeometries.
View ProjectarXiv-sorter
September 23, 2023 – Present
Sort the daily arXiv mail list by user keyworks, and output the manuscripts in a nice markdown.
View ProjectTunable-emission-metasurfaces
February 3, 2022 – February 8, 2022
Codes to reproduce the figures shown in the article: Tunable directional emission and collective dissipation with quantum metasurfaces.
View ProjectHEEM
April 21, 2021 – Present
Library to implement the Variational Quantum Eigensolver (VQE) using hardware-efficient entangled measurements to estimate the energy
View ProjectCultural Fit Analysis
The candidate's projects are heavily concentrated in academic/research-oriented computational physics and quantum computing. While demonstrating strong technical depth in these niche areas, the breadth of exposure to typical industry data science problems (e.g., machine learning, big data, A/B testing, business intelligence) is limited. This might indicate a potential gap in cultural fit for a general industry Data Scientist role, but could be a strong fit for research-focused data science positions.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's projects indicate a strong focus on independent research and development.