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TU Berlin invites applications for a Research Associate (Postdoc) in the Semiconductors and Microelectronic Systems group. The project focuses on computational quantum‑defect discovery, combining first‑principles simulations and machine learning to predict quantum sensor performance from atomic‑scale structures.
The ideal candidate has a PhD in a related field, strong DFT experience, and programming skills (Python/C/C++).
Reference number: IV-359/26 Published: 04.09.2026 Start date (earliest): Earliest possible, for 5 years Salary: Salary grade 13 TV-L Berliner Hochschulen Full/Part-time: full-time; part-time employment may be possible Application deadline: 02.10.2026
Faculty IV – Electrical Engineering and Computer Science, Institute of High-Frequency and Semiconductor System Technologies / Semiconductor components and microelectronic systems
Are you excited about discovering entirely new quantum defects and materials from first principles? Do you want to combine quantum materials simulation and artificial intelligence to build tools for designing next‑generation quantum sensors? The Semiconductors and Microelectronic Systems group at TU Berlin invites applications for a postdoctoral position at the intersection of computational materials science, artificial intelligence, quantum sensing and biomedical sensing.
We are seeking an outstanding postdoctoral researcher to help build a computational quantum‑defect discovery program aimed at identifying and engineering quantum sensors that outperform today’s state‑of‑the‑art platforms. Quantum spin defects in semiconductors, such as nitrogen‑vacancy (NV) centers in diamond, can detect magnetic fields with extraordinary sensitivity and spatial resolution, enabling transformative applications in biotechnology, neuroscience, semiconductor metrology and quantum technologies. The performance of these sensors is governed by material properties such as spin coherence (T₂), spin‑lattice relaxation (T₁), optical readout contrast (C), charge‑state stability and defect density. Despite remarkable progress, existing platforms operate well below the theoretical limits achievable for their material class. A central goal of the project is the development of a next‑generation computational discovery platform capable of predicting quantum‑sensor performance directly from atomic‑scale structure. The platform will combine first‑principles simulation and machine learning into an automated workflow for predicting quantum‑sensor performance, accelerating materials screening and designing new host‑defect systems.
To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities. Applications from people of all nationalities and with a migration background are very welcome.