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Technische Universität Berlin

Research Associate (PostDoc) – Computational Quantum-Defect Discovery Technische Universität Berlin in Germany

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

Deadline

Oct 31, 2026

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Country

Germany

University

Technische Universität Berlin

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Keywords

Computer Science
Chemistry
Electrical Engineering
Materials Science
Quantum Chemistry
High-throughput Screening
Density Functional Theory
High Performance Computing
Spin Dynamics
Quantum Sensing
Physics
Computational Materials Science

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About this position

Technische Universität Berlin is advertising a full-time Research Associate (PostDoc) position in the Research Center for Microperipheric Technologies, within Faculty IV – Electrical Engineering and Computer Science, Institute of High-Frequency and Semiconductor System Technologies / Semiconductor Components and Microelectronic Systems.

The project focuses on building a computational quantum-defect discovery platform for next-generation quantum sensors. The successful candidate will help develop first-principles and AI-enabled workflows that predict sensor performance directly from atomic-scale defect structure. The research combines density functional theory, machine learning, defect physics, and high-throughput screening to explore and optimize quantum defects in diamond, silicon carbide, boron nitride, molecular spin systems, and other emerging host materials.

This postdoctoral role is designed for an outstanding researcher with a strong background in computational materials science, quantum mechanics, and/or electrical engineering. The work targets quantum sensing applications with broad impact in biotechnology, neuroscience, semiconductor metrology, and quantum technologies. The position also involves collaboration with experimental partners in materials growth, device fabrication, quantum characterization, and biomedical sensing.

The appointment is funded at salary grade 13 TV-L Berliner Hochschulen and is advertised for a duration of five years. Part-time employment may be possible. The post is not funded through an EU programme. The earliest start date is 1 February 2027, with the application deadline set for 31 October 2026.

Applicants should hold a PhD or equivalent in a relevant area such as materials science, applied physics, electrical engineering, condensed matter physics, physical chemistry, or computational physics. Strong experience in DFT-based materials simulation is expected, together with research experience in at least two of the following: spin physics of point defects in semiconductors, multi-scale simulation methods, quantum chemistry for excited states, or machine learning for scientific discovery. Strong programming skills and the ability to teach in German and/or English are also required.

Applications must be submitted by email as one PDF file (maximum 5 MB) to [email protected], using the reference number IV-359/26. The application should include a cover letter in English, a CV with publication list if applicable, transcripts, and up to three letters of recommendation or reference contacts.

The position offers access to a young, ambitious, international research environment, high-performance computing resources, and collaborations with groups at Berlin Quantum Group, TU Delft, Institute of Neuroinformatics Zurich, EPFL Lausanne, UC Berkeley, and Lawrence Berkeley National Laboratory.

Funding details

Available

What's required

A completed university degree (Master, Diplom or equivalent) and a PhD, or equivalent, in Materials Science, Applied Physics, Electrical Engineering, Condensed Matter Physics, Physical Chemistry, Computational Physics or a related field are required. Applicants should have a strong background in first-principles computational materials science including DFT simulations using tools such as Quantum ESPRESSO, VASP, GPAW, CP2K or related packages. Research experience in at least two of the following is expected: spin physics of point defects in semiconductors (including spin-phonon coupling, zero-field splitting, hyperfine interactions, or spin relaxation and coherence), multi-scale simulation methods (DFPT, EPW, molecular dynamics, cluster correlation expansion, constrained DFT, DMFT, QMC or related approaches), quantum chemistry methods (CASSCF, multi-reference CI) for excited-state calculations of point defects, and machine learning for scientific discovery, materials informatics or AI-assisted materials design. Evidence of independent, high-quality research and publication is required, along with strong programming skills in Python, Fortran, C/C++ or related languages. The ability to teach in German and/or English is required, and willingness to acquire the missing language skills is expected. Experience with high-performance computing and workflow automation is desirable, as are strong communication, leadership and collaborative skills.

How to apply

Submit one bundled PDF by email to [email protected] using only the reference number in the subject line. Include a cover letter in English, CV in English, grade transcripts, and 1 to 3 recommendation letters or reference contacts. Send the application by 31 October 2026.

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