Sort by
Refine Your Search
-
Listed
-
Employer
- KTH Royal Institute of Technology
- Umeå University
- Chalmers University of Technology
- SciLifeLab
- Lunds universitet
- Umeå universitet stipendiemodul
- Blekinge Institute of Technology
- Lulea University of Technology
- Luleå University of Technology
- Umeå universitet
- University of Lund
- University of Skövde
- Uppsala universitet
- universitypositions
- 4 more »
- « less
-
Field
-
data analysis, such as signal processing, modeling, statistics, or machine learning Excellent written and verbal communication skills in English, particularly in a research context It is meriting to have
-
required to meet the eligibility criteria. Of secondary importance are: Experience with Python and relevant libraries for machine learning, optimization and simulation. Documented expertise in simulation
-
Qualifications The following qualifications and experience will be considered an advantage: Experience with crop modeling. Experience with plant breeding. Background in data science, machine learning, and
-
candidates whose expertise falls within one or more of the following areas: computational and mathematical modeling, statistical modeling, machine learning, network science, bioinformatics, applied mathematics
-
data and multimodal datasets combining imaging and molecular measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with
-
at the interface of automatic control, electrochemistry, and machine learning. The position will also involve close collaboration with another postdoctoral researcher working on a complementary project in physics
-
programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description We are looking for a postdoctoral fellow who wants to contribute to the next generation of
-
domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
-
on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
-
methods that combine soundscape targets, acoustic metamaterials, physical modelling, inverse design, machine learning and perceptual evaluation. The postdoc will develop models and design methods