Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Technical University of Denmark
- University of Southern Denmark
- Nature Careers
- Aarhus University
- University of Copenhagen
- Aalborg University
- ;
- ; Technical University of Denmark
- ; University of Copenhagen
- ; University of Oxford
- Copenhagen Business School , CBS
- European Magnetism Association EMA
- 2 more »
- « less
-
Field
-
candidate has proven expertise in X-ray imaging, synchrotron experiments, and multimodal X-ray imaging, e.g. XRD or XRF computed tomography. Experience in scripting in Python or MATLAB is an advantage
-
in C++ and Python, including experience with the scientific Python stack (NumPy, SciPy) and linear algebra/matrix libraries. Proficiency in developing efficient, maintainable code for engineering
-
benchmarking tools About you: You have prior knowledge of programming in python and are familiar with machine learning (ML) libraries in python. You have a strong interest in machine learning (ML) and deep
-
, catalysis or surface science. Experience in scientific programming, e.g. using Python, is an advantage. The candidate has obtained excellent grades in his/her Bachelor and Master educations, good
-
that provides a sufficient degree of background in computer science, artificial intelligence, mathematics and/or data science. Fluency in English and Python are required. Experience working with real-world
-
, fluid-structure interaction) Desire to develop interdisciplinary expertise across hydrodynamics and structural mechanics. Experience with or willingness to learn: Programming (e.g. C++, Python, Matlab
-
of the following areas: Research and development within computer vision and machine learning. Research and development within UAS platforms, subsystems, and payloads. Software design and development (C, C++, Python
-
hands-on experience in molecular cloning, cell culture systems, mouse models, and interest in liver disease biology. Experience with R or Python and the computational analysis of next-generation
-
include developing efficient code in Python and Julia to perform numerical optimization of control fields that govern the evolution of the photonic quantum information processor. This includes adaptation
-
implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use