Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Eindhoven University of Technology (TU/e)
- University of Twente
- Utrecht University
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Delft
- Eindhoven University of Technology (TU/e); Eindhoven
- University Medical Centre Groningen (UMCG)
- University Medical Centre Groningen (UMCG); Groningen
- University of Groningen
- University of Twente (UT)
- University of Twente (UT); Enschede
- Utrecht University; Utrecht
- Wageningen University & Research
- Wageningen University and Research Center
- Wetsus - European centre of excellence for sustainable water technology
- 5 more »
- « less
-
Field
-
mathematical modeling; fundamental understanding of fluid mechanics and soft matter physics; good quantitative skills and strong analytical capabilities; proven experience with experimental image and data
-
engineering, or in a closely related discipline; a strong and proven affinity with experimental techniques and mathematical modeling; fundamental understanding of fluid mechanics and soft matter physics; good
-
Vacancies PhD position on the design and fabrication of MEMS drag force-based flow and fluid composition sensors Key takeaways In this project, we will combine well-known thermal flow sensing
-
for donor kidneys. Central to this is the use of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies
-
of microorganisms in fluids. Living systems such as bacteria or algae exhibit remarkable capabilities: they swim, adapt, interact, and self-organize into dynamic patterns. Understanding and replicating these life
-
. Candidates with experience in high-voltage / plasma research are preferred. Knowledge of computational fluid dynamics is a plus. Strong organisational and communication skills are expected in order to
-
to the proportion and composition of mineral, melt and fluid phases across a range of geologically-relevant pressure, temperature and composition. With constraints on the partitioning of trace elements among
-
/microrobots that can move and interact autonomously in 3D environments, mimicking the complex dynamics of microorganisms in fluids. Living systems such as bacteria or algae exhibit remarkable capabilities
-
of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ
-
microswimmers/microrobots that can move and interact autonomously in 3D environments, mimicking the complex dynamics of microorganisms in fluids. Living systems such as bacteria or algae exhibit remarkable