211 machine-learning-"https:" "https:" "https:" "UCL" "UCL" Postdoctoral positions in Denmark
Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
The Daasbjerg research group at the Department of Chemistry, Aarhus University, is seeking a candidate for a 31-month postdoctoral position. This position focuses on AI/machine learning to develop a
-
following thematic areas: • AREA 1: Machine learning and AI-driven methods for design, simulation, and optimisation in architectural and construction engineering. • AREA 2: Robotic and additive
-
opportunities to explore the intersections between these disciplines. Read more here: https://www.sdu.dk/en/om-sdu/institutter-centre/fysik_kemi_og_farmaci/ominstituttet Application, salary etc. The successful
-
scientific staff and 85 PhD students. English is the primary language used for internal communication and teaching, and international candidates are not required to learn Danish. Aarhus University offers a
-
for the human societies https://www.sdu.dk/en/forskning/sdu-climate-cluster . The work language is English. If the application is successful, the postdoc candidate will be based at SDU with opportunity of a
-
sharing, and professional development. Learn more about AAU Energy at www.energy.aau.dk. How to apply Your application must include the following: Application, stating reasons for applying, qualifications
-
be to: Conduct multidisciplinary research (as explained above) Teach (and design) BSc and MSc courses, Supervise BSc and MSc student projects, Supervise PhD students as a co-supervisor for PhD students
-
written and spoken Willingness to engage in interdisciplinary collaboration and fieldwork Advantageous: Knowledge of bat ecology and species identification Experience with machine learning or automated
-
field soil and will be conducted as part of the N2CROP project [https://mbg.au.dk/n2crop ]. Your profile We are looking for a highly motivated candidate with a keen interest in legume-rhizobium
-
at the intersection of AI, RF, and wireless communication. Your main tasks include developing machine-learning methods for wireless interference detection, mitigation, edge intelligence, and applying AI to optimize RF