57 data "https:" "https:" "https:" "UCL" "UCL" PhD positions at Aalborg University in Denmark
Sort by
Refine Your Search
-
, mathematical engineering, acoustics, machine learning or similar; Solid mathematical and analytical skills, including signal processing, optimization, machine learning or information theory; Experience in
-
disturbances or cyberattacks, such as sensor manipulation, electromagnetic interference, or injected faults, can affect the behaviour of power electronic systems. Developing data-driven models that capture how
-
of student projects and participation in courses related to human-computer interaction and software engineering. Your competencies Applicants should have a strong interest in human-robot interaction and the
-
will be part of a team of researchers responsible for the annual productivity studies by Aalborg University Business School. These studies provide data driven insights into regional productivity
-
that can support the development and implementation of robots that meaningfully support everyday work in healthcare. The successful candidate will conduct empirical research and collect data through
-
the agricultural pest, Spotted Wing Drosophila in current and future climates. This includes exploring mechanistic distribution models involving experimental data on environmental tolerances such as
-
reliability. Prospective applicants for this PhD proposal should have the following qualifications: M.Sc. degree in communications engineering, mathematical engineering, electrical engineering, computer
-
, reviewing of literature, experimental work, modelling, data analysis, writing etc. The project can to some extent be tailored to the candidate’s interests and expertise. The PhD student will follow courses
-
of the jobpost. Further information Read more about our recruitment process here. The assessment of candidates for the position will be carried out by qualified experts. Shortlisting will be applied. This means
-
of these materials. Implementation of artificial intelligence (AI) and machine learning (ML) to establish the connection between the existing models and material data (both literature and the baseline established in