21 algorithm-"Prof" "NTNU Norwegian University of Science and Technology" PhD positions at Technical University of Denmark in Denmark
Sort by
Refine Your Search
-
experimentation with Asst. Prof. Eli N. Weinstein. Your goal will be to develop fundamental algorithmic techniques to overcome critical bottlenecks on data scale and quality, enabling scientists to gather vastly
-
will take advanced courses to build and deepen your skills, implement and evaluate algorithms, and develop your ability to write and present scientific work. We are a supportive team that will welcome
-
. You will work under the supervision of Prof. Francisco C. Pereira, Assoc. Prof. Carlos Lima Azevedo (DTU), Dr. Biagio Ciuffo and Dr. Georgios Fontaras (JRC). You will work on research focused
-
our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by Prof. Henning
-
. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by Prof
-
agricultural robotics and new sustainable farming practices. The PhD projects will be combining new sensor systems and perception algorithms. So, if you are one of the 2 selected applicants, your primary
-
photonics’, led by Assoc. Prof. Thomas Christensen, who moved from MIT to DTU in 2023. Funded by a Villum Young Investigator program (link ), the project aims to uncover novel kinds of photonic topology using
-
of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by Dr. Lei Yang and Prof. Johannes Kabisch (Norwegian University of Science
-
experimental research in nanoparticle catalysis using advanced operando electron microscopy This collaborative PhD project between Technical University of Munich (TUM) ( the group of Prof. Barbara A.J. Lechner
-
achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing