34 parallel-programming "EPIC" Postdoctoral research jobs at Technical University of Denmark in Denmark
Sort by
Refine Your Search
-
part of a team, be happy to share thoughts and receive feedback from other team members, and be able to carry out parallel tasks In addition, it is preferable that you: Have experience with processing
-
meet security, compliance, and integration requirements. You will collaborate with various professionals and act as the link between technical teams, clinical teams, and project management. In parallel
-
airborne geophysical observations, and strong competences in Arctic fieldwork logistics. The gravity research group has carried out airborne gravimetry since the 1990s and has an annual work programme to
-
. Knowledge of compilers, especially an LLVM-based compiler tool chain, program analysis, and computer architecture. Knowledge of real-time systems. Systems programming and C/C++. We offer DTU is a leading
-
Job Description The Quantum and Nanophotonics Section at DTU Electro is seeking a highly motivated postdoc to be a part of a program on ‘Symmetry-guided discovery of topological photonics’, led by
-
you are open to working with other colleagues. Qualifications You can work independently, plan and execute complicated and challenging tasks. You are flexible, self-motivated, interested in
-
Solid programming experience, preferably in Python Familiarity with structured data handling (e.g., SQL) and scientific workflows Documented experience with ontology development, knowledge graphs, and
-
Development and Demonstration Programme. The position is affiliated with the Section for Mechanical Technology and will be under the supervision of Associate Professor Jonas Sundberg. The project focuses
-
postdocs. You will join a new research program on the role of universities and entrepreneurial ecosystems in building deep tech startups and scaleups. You will work towards building a landmark database on
-
, TESPy, or similar libraries. Strong programming skills in Python or MATLAB, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit