Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Chalmers tekniska högskola
- Lunds universitet
- Umeå University
- University of Lund
- Umeå universitet stipendiemodul
- Uppsala universitet
- Karolinska Institutet (KI)
- SciLifeLab
- Umeå universitet
- Örebro University
- KTH
- Linköpings universitet
- chalmers tekniska högskola
- Chalmers Tekniska Högskola
- European Magnetism Association EMA
- Karolinska Institutet
- Linköping University
- Linköping university
- Linköpings University
- Linneuniversitetet
- Nature Careers
- Sveriges Lantrbruksuniversitet
- 14 more »
- « less
-
Field
-
statistics, unsupervised machine learning, optimisation, model predictive control. Experience in financial mathematics. Having high integrity, be process-oriented and able to work independently. Being able
-
. Teaching may also be included, but up to no more than 20% of working hours. The position includes the opportunity for three weeks of training in higher education teaching and learning. The purpose
-
integration of AI-based vision and active machine learning to optimize the efficiency of the process. Writing publications and present research results from the project on conferences. Collaboration with
-
. Documented knowledge and experience in computational metabolomics, computational biostatistics, statistical and machine learning, involving analysis of biological multi-modal and multivariate data, or related
-
for the doctoral degree. Exceptions from the 3-year limit can be made for longer periods resulting from parental leave, sick leave or military service. The following experience will strengthen your application
-
science. You will be part of a dynamic research group with expertise in Earth Observation, geoinformatics, and machine learning, offering an excellent environment for advancing your research and building
-
funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in developing new image analysis and machine learning methods
-
methods to rigorously assess the safety and effectiveness of medications in real-world patient populations. Defining individualized treatment strategies: Leveraging traditional and causal machine learning
-
, statistical and machine learning, involving analysis of biological multi-modal and multivariate data, or related fields, is a requirement. Experience with computational modeling in metabolomics and metabolic
-
consists of 18 research groups covering a wide range of mathematical disciplines – from pure and applied mathematics to numerical analysis and optimization, as well as mathematical statistics and machine