108 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" positions at Aarhus University in Denmark
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
and applied mathematics and offers a dynamic and collegial academic atmosphere. More information about the department can be found at https://math.au.dk. Place of Work and Area of Employment The place
-
see: http://ecos.au.dk/en/ . What we offer The department offers: A multi-disciplinary research environment collaboration within strong research teams with extensive experience in carbon flux research
-
to new projects. Assist with troubleshooting their machines and support their understanding of core concepts. Guide students in working on their own project. Demonstrate best practices and foster
-
qualifications include: Ph.D. in Computer Science, Computer Engineering, Electrical Engineering or a related field; Strong background in Deep Learning (e.g., Transformers, foundation models); Strong programming
-
the area of employment is Aarhus University with affiliated institutions. Contact information For further information, please contact: Senior Advisor, PhD, Lise Bonne Guldberg, +45 4189 3200, lbg
-
Allé 20, 8830 Tjele. The area of employment is Aarhus University with affiliated institutions. Contact information For further information, please contact: Morten Ambye-Jensen, +45 93 50 80 09, maj
-
Department of Electrical and Computer Engineering (ECE), Aarhus University (AU) invites applications for a position as Tenure Track Assistant Professor/Associate Professor in electronics
-
affiliated institutions. Contact information For further information, it is possible to contact Mads Bendixen from 12 January to 16 January 2026. You can contact him on mlb@corc.au.dk or +45 30 34 43 64
-
/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning approaches, and development
-
key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and