Sort by
Refine Your Search
-
qualifications As our new colleague in our research team your job will be to develop novel computational frameworks for machine learning. In particular, you will push the boundaries of Scalability, drawing upon
-
degrees in either the natural sciences (chemistry, physics, mathematical/computational biology) or in the formal sciences (statistics, computer science, mathematics), but must have a serious interest in
-
DTU Management’s Management Science division. The project is led by Professors Stefan Ropke and Richard Lusby and involves international collaboration with leading researchers in machine learning and
-
Job Description We invite applications for a fully funded 3-year PhD position in the Embedded Systems Engineering (ESE) research section at DTU Compute in collaboration with the Technical
-
characterization aspect of the project, i.e. investigation of dynamics during catalyst activation and reaction by in-situ transmission electron microscopy. VISION is pioneering technology for visualizing catalytic
-
group and are expected to contribute to other departmental tasks. We expect that you have a background in biology or veterinary medicine and have an interest in interactions between diet, intestinal
-
, including electrical engineering, control theory, industrial engineering, electronics engineering, energy policy, data science, and applied mathematics. As part of the Alliance program, your project will be
-
of Science and Technology (NTNU) offers a joint 3-year PhD fellowship. Novel non-target chemical analyses have recently revealed that groundwater and drinking water are contaminated from PFAS, pesticide and
-
deformation. Responsibilities Develop scientific machine learning methods in close collaboration with team members specializing in experimental techniques and materials science. Utilize unique experimental data
-
Job Description The Quantum and Nanophotonics section at DTU Electro is seeking an excellent and highly motivated PhD student to be a part of a program on ‘Symmetry-guided discovery of topological