Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Aalborg University
- Aarhus University
- University of Copenhagen
- Aalborg Universitet
- Aarhus University (AU)
- Technical University of Denmark (DTU)
- Technical University of Denmark
- Copenhagen Business School
- Technical University of Denmark;
- TEGNOLOGY APS
- Technical University Of Denmark
- University of Southern Denmark (SDU)
- 2 more »
- « less
-
Field
-
The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for a PhD stipend in the field of secure machine learning within the general study programme
-
professors, two postdocs, and five PhD-students. The group focus on high-quality applied research. The current topics of interest in the group include student learning, transitions and career, teacher
-
of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
-
computational methods as the necessary foundation to venturing and expanding the field through modern approaches in machine learning and artificial intelligence. The appointment is hosted by the Department
-
advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
-
-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
-
Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong
-
Perception Lab. Your competencies You have a strong background in computer vision and artificial intelligence, documented by a relevant PhD degree in, for example, computer science, machine learning, computer
-
industrial energy systems that combine physics and data to become adaptive, autonomous and trustworthy? To get there, you will work at the intersection of thermal energy systems, machine learning and
-
statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability