Sort by
Refine Your Search
-
Employer
-
Field
-
must include: a presentation of an original research question a description of the initial theoretical framework and method a presentation of the proposed material a work plan for the project Application
-
; survey design and online experimental methods; quantitative data analysis, preferably including choice modelling, willingness-to-pay analysis, segmentation, multivariate statistics, or related methods
-
duration of employment This is a 5-month position (30 hours/week) from 01 November 2026. Job description You will be contributing to the development of catalytic methods based on transition metal catalysis
-
and different industrial outreach activities The candidate has at least the following qualifications - Applicants should hold a PhD in Computer Engineering, Computer Science, or similar - Cyber-physical
-
statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability
-
team, Aarhus University and MAX IV. Depending on the candidate’s interests and expertise, research activities may include studies of functional materials, advanced crystallographic methods, automated and
-
and work in the interface of basic cellular biology /virology and experimental clinical medicine trials meeting the challenges of developing methods to the highest international scientific standards As
-
instruments, with data obtained with DNA methods and data obtained with a Hirst trap. • Numerical models used by the group covers the particle dispersion model HYSPLIT in combination and the numerical models
-
candidate will have an existing research profile that uses ethnographic methods to explore questions relevant to the social and ecological effects of livestock and agricultural infrastructures on landscapes
-
genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods