248 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Ulster-University" positions in Switzerland
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
of electron spectroscopy experiments Modelling of experimental data Your profile The position is immediately available for a candidate with a master's degree (or equivalent) preferably in physics, physical
-
stage, nor is it intended to support doctoral dissertations. We also encourage small interdisciplinary teams of up to three people to apply with a joint project. Please find more information here about
-
. As part of your role, you will: Take technical ownership of spacecraft avionics subsystems, acting as the primary technical authority for onboard computing and data handling within the assigned project
-
institutions worldwide. Further information about Professorship for International Relations and Data Science can be found on our website. Questions regarding the position should be directed to Professor Dr
-
/certificates Contact information of 2-3 referees A short description of research interests Please note that we exclusively accept applications submitted through our online application portal. Applications via
-
AVA with your UZH login (short name and password). If you do not (yet) have a UZH login, an administrative employee of the host institution can enter the project data in AVA on your behalf and then
-
research achievements and research interests Detailed CV Transcripts of all degrees (English) Names and contact information of at least 2 references Representative published research work (Papers or thesis
-
involve team-based, open-source software development to build planning automation tools and digital information exchange between AEC disciplines. Specific topics of investigation may include computational
-
portal. The deadline for applications is the 3rd of April, 2026, with a start date of Summer-Autumn 2026. Further information about our groups can be found on the Planetology and High Pressure Group
-
to efficiently train on the large, densely-connected and graph-structured data encountered in our systems of interest. Your contributions would be across the spectrum from methodological development