Sort by
Refine Your Search
-
Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater
-
opportunity to join the ERC-funded project “ALPS - AI-based Learning for Physical Simulation”. Expected start date and duration of employment These are 1–year positions from 1 May 2026 or as soon possible. Job
-
are expected to: shine in individual and collaborative research, either to assist groups of bachelor’s students in doing homework or co-teach advanced courses relevant for your research area. The
-
expected to: shine in individual and collaborative research, either to assist groups of bachelor’s students in doing homework or co-teach advanced courses relevant for your research area. The Department
-
. Furthermore, to be highly skilled with strong learning abilities and a positive mindset, it is expected that the candidate will lead all aspects of the project. A profound interest in both the methodological
-
or similar. Experience in handling dynamic modelling and control, experimental setup and testing, Digital Twin and Machine Learning Publication experience Collaboration and/or management skills Communication
-
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
-
/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning approaches, and development
-
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
-
100 university. Aarhus BSS has achieved the triple-crown AACSB, AMBA and EQUIS Further information If you have any questions regarding the position or want to learn more about the project and specific