Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
leading universities, research institutes, and industrial partners across Europe to deliver a world-class doctoral training program in risk assessment, resilience engineering, and smart technologies. Its
-
within chemistry, physics, medical biology, molecular biology, environmental sciences and mathematics. Bachelor programs are offered within these areas, as are interdisciplinary Master programs
-
problem-solving skills, with a solid foundation in Mathematical, Probabilistic and Engineering principles and methods. Familiarity with offshore engineering and wave-wind theories is advantageous
-
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
-
equivalent to a two-year master's degree. Your academic background needs to be relevant to the above-stated project objectives, e.g., civil engineering, mechanical engineering, physics, or applied mathematics
-
26 Aug 2025 Job Information Organisation/Company Technical University Of Denmark Department DTU management Research Field Engineering Economics Researcher Profile First Stage Researcher (R1
-
institutes, and industrial partners across Europe to deliver a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its scientific vision targets: (1
-
– Department of Mathematics and Computer Science – is an internationally recognised academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics
-
Job Description The Climate and Energy Policy Division at DTU's Department of Technology, Management and Economics offers a three-year PhD position in the Energy Economics and Modelling section
-
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