4 Doktorstelle Jobs in Wien
Ihr Aufgabenbereich
- Diploma or Master’s degree in Physics or a related discipline
- A strong interest in quantum technology, demonstrated e.g., by a Master thesis in these topics, is desirable
- Excellent English language and communication skills
- Research experience with defects in crystals, such as nitrogen-vacancy centers in diamond
- Laboratory experience including optics, spectroscopy, data acquisition and evaluation
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Ihr Aufgabenbereich
- Conduct original scientific research in the field of inverse problems applied for extremely large telescopes (theoretical analysis, development and implementation of algorithms, simulations and experimental tests) Be part of a doctoral training and work towards obtaining a PhD in technical sciences
- Collaborate with scientists and students working in other fields covered by the mathematical and astronomical research network
- Publish scientific findings in renowned international journals and at conferences
- Complete trainings or short-term research stays at international collaboration partners
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Vollzeit | Freelancer, Projektarbeit
Your Tasks
- The successful candidate will analyse data taken with the NUCLEUS experiment during the commissioning phase and physics runs.
- A main aspect of the work will be the improvement of NUCLEUS event reconstruction and event selection using Artificial Intelligence (AI) methods, with application in the subsequent analysis.
- In addition, the application of the AI methods for detector setup, operation, and optimization for NUCLEUS data taking could be performed.
- Contributions to the commissioning and operation of the NUCLEUS experiment at its experimental site in Chooz, France are expected.
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Your Tasks
- Develop and merge innovative approaches in spatial data encoding, continuous output representation, and multi-variable simulation with dynamic data integration (Retrieval Augmented Generation, RAG) to push the boundaries of spatial stochastic simulation research.
- Design, prototype, and test advanced transformer-based methods tailored to complex spatial and multi-variable data.
- Create robust training protocols to manage non-stationary data and develop strategies for continuous output (e.g., raw value predictions, Fourier decomposition).
- Implement tokenization and cross-attention techniques to efficiently handle multi-variable simulations.
- Collaborate with international partners and contribute to an environment that values scientific freedom, interdisciplinary work, and curiosity-driven exploration.
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