44 parallel-computing-numerical-methods-"DTU" Postdoctoral research jobs at Nature Careers in Denmark
Sort by
Refine Your Search
-
effort within DTU Bioinformatics to develop bioinformatics, immuno-informatics, metagenomics and systems biology methods and solutions to the challenge of handling and interpreting large scale and
-
immediate biomedical relevance Access state-of-the-art labs and infrastructure at DTU Health Tech Collaborate with an interdisciplinary team of chemists, engineers and biologists Develop your academic career
-
to develop new scientific methods to design and analyse better, more sustainable buildings. A researcher at the turn of the last century was trying to find a standardized animal model for their experiments. A
-
join the UPCYFUN project at DTU as a Postdoctoral Researcher. UPCYFUN is a collaboration between DTU, Aalborg University, TDI and the private companies ProteinFrontiers, Pexinno, HNH-consult, Lyras, and
-
The section for Luminescence Physics and Technologies (LUMPHYS) at the Department of Physics, Technical University of Denmark (DTU), invites applications for a 2-year postdoctoral position
-
Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
-
. The position focuses on frequency-domain electromagnetic (FEM) and transient electromagnetic (TEM) methods. The successful candidate will contribute to the development of an inversion framework for the joint
-
PhD degree for the topic. We offer DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and
-
, Fortran, or other relevant languages. Knowledge of statistical methods for climate data analysis. Experience with high-performance computing (HPC) environments. A strong publication record relative
-
contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with