91 computer "https:" "https:" "https:" "https:" "https:" "UCL" Postdoctoral positions in Denmark
Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
all qualified candidates regardless of personal background. Apply online https://fa-eosd-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/jobs/preview/4131 Share this job
-
read more about VISION at www.vision.dtu.dk and DTU Physics at https://physics.dtu.dk/ If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU
-
-art infrastructure and a strong commitment to scientific excellence. More information: https://mbg.au.dk What we offer We offer: The opportunity to contribute to an internationally recognized
-
electronically along with your application. Aarhus University also offers a Junior Researcher Development Programme targeted at career development for postdocs at AU. You can read more about it here: https
-
candidates regardless of personal background. Apply Online https://fa-eosd-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/job/4311/?utm_medium=jobshare&utm_source
-
development for postdocs at AU. You can read more about it here: https://talent.au.dk/junior-researcher-development-programme/ If nothing else is noted, applications must be submitted in English. The
-
career counselling to expat partners. Please find more information here: https://internationalstaff.au.dk/relocationservice/ Please find more information about research opportunities at Aarhus
-
Computer Science Ref number: 2026/958 Employment type: Full Time Apply Aalborg Deadline : 31.08.2026 Department: Department of Computer Science Ref number: 2026/958 Employment type: Full Time Apply Vacant
-
Join us at the Department of Electrical and Computer Engineering at Aarhus University for a two-year postdoctoral position focused on physics-driven machine learning for ground-based, airborne, and
-
the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum