Sort by
Refine Your Search
-
Country
-
Employer
-
Field
-
(R2) Positions PhD Positions Application Deadline 26 Apr 2026 - 23:59 (Europe/London) Country United Kingdom Type of Contract Temporary Job Status Full-time Hours Per Week 37 Is the job funded through
-
international work environment Learn more about CQT at https://www.cqt.sg/ Job Description The CQT S14 team is looking for candidates with strong background in Software Engineering, Computational Physics
-
experimental platforms for real-world quantum computing applications. Candidate Profile We are looking for interested candidates who have: - PhD in Computer Science, Software Engineering, or a related field
-
for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as follows
-
from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as follows: The average grade point for courses included in
-
interpret the architecture to local field potential data recorded in humans who have seen a vast number of images from the CoCo-database (https://cocodataset.org ); and apply and interpret the architecture
-
to ensure project deliverables are met. Any other adhoc duties assigned by supervisor. Job Requirements PhD/Master’s in Naval Architecture, Ocean Engineering, Civil Engineering, or related field. Proficiency
-
by Supervisor. Job Requirements Master/PhD in Naval Architecture, Ocean Engineering, Marine Engineering, Civil Engineering, or related field. Proficiency in hydrodynamic modeling tools (e.g., ANSYS
-
existing financial IT infrastructures, could be another potential direction for this PhD project. For example, this could involve studying system architectures that integrate AI with legacy platforms
-
Enhancement of AI/ML with in-network computing & processing Adaptation & optimization of AI/ML software libraries for non-conventional hardware architectures Physics-informed ML surrogates for efficient