28 wireless-sensor-networks PhD positions at Technical University of Denmark in Denmark
Sort by
Refine Your Search
-
on the development of micro and nanotechnology-based sensors, detection systems, drug-delivery devices, and energy materials. Responsibilities and qualifications You will be responsible for the fabrication and
-
, nanophotonics, lasers, quantum photonics, optical sensors, LEDs, photovoltaics, ultra-high speed optical transmission systems, bio-photonics. Technology for people DTU develops technology for people. With our
-
failure analysis using advanced finite element models and simulation techniques. This is enabled by digital and sensor technologies such as artificial intelligence, computer vision, drones, and robotics
-
, quantum photonics, optical sensors, LEDs, photovoltaics, ultra-high speed optical transmission systems, bio-photonics, acoustics, power electronics, robotics, and autonomous systems. Technology for people
-
with many opportunities for professional development and global networking. Responsibilities and qualifications We are seeking a PhD student with background and interest in enzymology, molecular biology
-
professional network through international research stays and develop innovative solutions that support the global transition to sustainable energy systems. Apply now to make a difference in the field
-
. Responsibilities: Conduct research in time-predictable computer architecture. Designing a network-on-chip for real-time automotive systems Verify the design with modern verification methods, such as function
-
. Fluency in English (oral and written). Excellent communication abilities, social skills, and independent attitude paired with the ability to network and interact with relevant stakeholders. We envision
-
Job Description We are seeking candidates for a 3-year PhD project as part of the European Marie Skłodowska-Curie Actions Doctoral Network on “Consumer Energy Demand Flexibility in Electricity Use
-
learning approaches and develop a theoretical understanding potentially based on differential geometry. In particular, deep neural networks perform surprisingly well on unseen data, a phenomenon known as