Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Eindhoven University of Technology (TU/e)
- Tallinn University of Technology
- University of Siegen
- Aalborg Universitet
- Aalborg University
- Delft University of Technology (TU Delft)
- ETH Zürich
- GFZ Helmholtz-Zentrum für Geoforschung
- INESC ID
- Leiden University
- NTNU - Norwegian University of Science and Technology
- Simula UiB AS
- Swansea University
- Technical University of Denmark
- The University of Auckland
- University of Exeter;
- University of Jyväskylä
- Universität Siegen
- Uppsala universitet
- 9 more »
- « less
-
Field
-
for a researcher as of the Feb. 1st, 2026 (PhD position) as part of the reserach project BANNER. Your tasks: Develop AI algorithms for real-time fault detection, fault classification, and failure-mode
-
Description The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for one or more PhD stipends in the field of Protocols and methods for quantum error correction
-
the 01.02.2026 at the following conditions (PhD position): 50% = 19,92 hours Pay grade 13 TV-L limited 30.11.2028 Your tasks: Develop AI algorithms for real-time fault detection, fault classification
-
of LLMs is desirable. Basic understanding of embedded systems. Familiarity with fault detection, system reliability, or troubleshooting techniques. Strong programming skills (C/C++, Python; hardware
-
for electrical machines based on custom-designed embedded hardware and edge-deployable AI models. Building upon prior work that established basic IoT connectivity and AI-based fault detection methods
-
for geochemistry, geophysics, biogeochemistry, planetary habitability, and sustainable energy resources. The successful candidate will join a dynamic research team investigating how crustal faulting, tectonics, and
-
) provides more in-depth information about Simula culture. Where to apply Website https://www.simula.no/careers/job-openings/phd-student-in-quantum-error-correct… Requirements Research FieldComputer science
-
learning and physics-based approaches for battery life extension. Develop AI-based predictive maintenance techniques for early fault detection and anomaly detection in battery systems. Implement multiscale
-
(as a pack consists of a large number of cells) Fault detection and predictive maintenance. As fleet owners continuously monitor the batteries in their fleet, methods are needed that can predict
-
solvers, finding better error correction protocols using combinatorial reasoning, processing quantum information using knowledge compilation approaches, and exploring the potential of satisfiability