98 software-verification-computer-science PhD positions at Technical University of Denmark
Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
following qualifications: Programming skills and AI interest Data analytical skills and computer science focus Experiences with chemical analysis and pilot experiments is an advantage. Knowledge
-
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
-
) or a similar degree with an academic level equivalent to a two-year master's degree in bioinformatics, computational biology, evolutionary genomics, or a related field Proficiency in programming
-
bioinformatics, AI and ML software tools to integrate and process the datasets quickly and efficiently. You will also work closely with other computational and experimental biologists to uncover new insights
-
Job Description The Structures and Safety Section at DTU Civil and Mechanical Engineering Department is looking for an ambitious, motivated and skilled PhD student eager to dedicate three years
-
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
-
competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
-
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
-
process. Together, these innovations aim to make column generation more practical for solving real-world, large-scale optimization problems. These innovations will be tested within a structured software
-
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