Sort by
Refine Your Search
-
Listed
-
Employer
- Technical University of Denmark
- University of Southern Denmark
- Aalborg University
- Nature Careers
- Technical University Of Denmark
- University of Copenhagen
- DTU Electro
- Aalborg Universitet
- Aarhus University
- Copenhagen Business School
- Danmarks Tekniske Universitet
- COPENHAGEN BUSINESS SCHOOL
- Copenhagen Business School , CBS
- NKT Photonics
- Technical University of Denmark (DTU)
- University of Groningen
- 6 more »
- « less
-
Field
-
research areas are explored, Microbes and Element cycles; Deep-sea research, Co-evolution of Life and Earth, Oxygen, Greenhouse gases and climate, Sustainability and biotechnology: How microorganisms
-
robotic control strategies that ensure precise, safe, and patient-friendly probe positioning. The candidate will be responsible for developing artificial intelligence methods for cardiac view identification
-
capacity to relate theoretical topics to practical challenges and solutions, also in policies. Excellent skills in quantitative research methods and data handling. Experience in working with large individual
-
to work closely with our academic and industrial partners on application-oriented methods and technologies, with the support of the unique set of equipment of the center. LSP is closely working together
-
for Quantitative Sustainability Assessment of the Department of Environmental and Resource Engineering (DTU Sustain). The section spearheads the development of sustainability assessment methods. You will become part
-
Nordisk Foundation. The research work involves the synthesis and characterization of novel compounds based on mixed-anion compounds (e.g., transition metal (oxy)nitrides) and the evaluation
-
with both academic researchers and industry experts across Europe. Your key responsibilities will include: Conduct analysis of polymer membrane waste materials Develop electrospinning methods to produce
-
with expertise in digital signal processing methods, and machine learning methods for amplitude and phase noise characterization of optical frequency combs, recovery of dual-comb measurement signals and
-
student will become part of a team at DTU with expertise in digital signal processing methods, and machine learning methods for amplitude and phase noise characterization of optical frequency combs
-
methods for experimental validation of near-field interaction Fabrication, measurement, and assessment of one or more prototypes Dissemination of scientific results in highly reputed journals and