Current PhD projects at SDU UAS Center
Aerialcore/Drones4Safety
PhD Student: Viet Duong Hoang
Principal supervisor: Emad Ebeid
Project poster is on its way
Genius
PhD Student: Jes Hundevadt Jepsen
Principal supervisor: Ulrik Pagh Schultz
Co-supervisor: Kjeld Jensen
Project poster is on its way
Drones4safty/Drones4Energy
PhD Student: Frederik Falk Nyboe
Principal supervisor: Emad Ebeid
Project poster is on its way
Drones4safty/Drones4Energy
PhD Student: Nicolai Haarhøj Malle
Principal supervisor: Emad Ebeid
Project poster is on its way
Autonomous Service Drones for Safety-Compliant European Operations
PhD Student: Kristian Husum Terkildsen
Principal supervisor: Kjeld Jensen
Hardware-centric Modular Framework for Continuous Testing of Autonomous Cyber-physical Systems
PhD Student: Martin Skriver
Principal supervisor: Anders Stengaard Sørensen
Co-supervisor: Ulrik Pagh Schultz
Concluded PhD projects
Power Modeling and Optimization of UAV Software for Heterogeneous Embedded Systems
PhD Student: Adam Seewald
Principal supervisor: Ulrik Pagh Schultz
An Ethical Drone
Concluded PhD project
PhD student: Dylan Cawthorne
Principal supervisor: Alf Mikael Rehn
Co-Supervisor: Marianne Harbo Frederiksen and Douglas Cairns
Developing a commercial drone based on philosophical, social, and technological considereations.
Safe Multi-sensor Software Architecture for Autonomous Vehicles
Concluded PhD project
Industrial PhD student: Johann Thor Ingibergsson Mogensen
In cooperation with: CLAAS E-Systems
Supervisor: Ulrik Pagh Schultz
The project aims to design and develop hard- and software adhering to international safety standards and, in particular, the development of a method for automatically integrating sensor data and automatic program generation to ease the integration and verification of sensors and software.
Machine Vision Based Plant Recognition for Weed Species Identification
Concluded PhD project
PhD student: Mads Dyrmann
Supervisor: Henrik Skov Midtiby
The purpose of the PhD project is to develop a system, which can partly or fully automate the process of weed species identification in images acquired from conventional grown fields.
PhD project poster