Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- Swedish University of Agricultural Sciences
- Blekinge Institute of Technology
- KTH Royal Institute of Technology
- SciLifeLab
- University of Lund
- Karlstad University
- Karolinska Institutet (KI)
- Luleå tekniska universitet
- Lunds universitet
- Institutionen för biologi och miljövetenskap
- Linköping University
- Lulea University of Technology
- Sveriges Lantbruksuniversitet
- Umeå University
- University of Borås
- Uppsala universitet
- universitypositions
- 8 more »
- « less
-
Field
-
, psychology, and forestry. The candidate will support the integration of behavioral science principles and natural resource management knowledge into relevant research designs; developing research questions
-
that the system does not support Zip files. CV A comprehensive CV, including a complete list of publications. Details of previous teaching and pedagogical experience. Personal letter A brief introduction
-
to recruit a postdoctoral researcher to join researchers in applied economics, psychology, and forestry. The candidate will support the integration of behavioral science principles and natural resource
-
ways of working. Up to 20% of the total work time may be related to other tasks than research, such as teaching, supervision of student projects, and support of doctoral education activities
-
time, with the aim of developing models, tools and competitive technological solutions to support the transition in sectors such as energy, transport and heavy industry. Within a scientifically excellent
-
, or reinforcement learning. Experience with high-performance computing (HPC). Experience supervising students or junior researchers. What you will do As a postdoctoral researcher, you will: Develop machine-learning
-
substantial challenges for the efficiency, durability and lifecycle cost of battery-supported charging systems. This project investigates advanced reconfigurable and AC-native battery architectures for high
-
of your thesis. Experience of working with optimisation methods, long-term modelling of the forest landscape and its ecosystem services, as well as computer-based decision support systems in general
-
science, or a related field. Experience in energy transitions research Strong analytical skills, including proficiency in quantitative modelling, data analysis, and scientific computing (e.g., in R, Python
-
HIBEAM/NNBAR at ESS and – most relevant for this project – LDMX at SLAC (https://confluence.slac.stanford.edu/display/MME/Light+Dark+Matter+Experiment ). We exploit synergies across these projects, and our