Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Linköping University
- Umeå University
- Uppsala universitet
- Chalmers University of Technology
- Linköpings universitet
- SciLifeLab
- Stockholms universitet
- Umeå universitet
- KTH Royal Institute of Technology
- Luleå University of Technology
- Lunds universitet
- Nature Careers
- Sveriges lantbruksuniversitet
- Swedish University of Agricultural Sciences
- 4 more »
- « less
-
Field
-
sciences . Project description The Johannesson lab at DEEP makes use of the unique lifestyles of fungi to explore evolutionary questions about individuality and genetic inheritance. The group is now looking
-
education at the department occurs in an international environment and is focused on animal biology. Outstanding and high impact research is conducted in a variety of fields, including evolutionary biology
-
Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Our research group studies the ecological and evolutionary drivers of floral
-
, algorithms, and programming. Knowledge and experience in artificial intelligence and machine learning is expected, but not required. Knowledge and experience in deep learning and generative AI is considered
-
; they make sense to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project
-
, signal processing and/or wireless communication. Basic knowledge of and/or experience in working with reinforcement learning/other machine learning algorithms Excellent command of spoken and written
-
address outstanding questions on behavioural evolution in canids. Your work assignments Understanding how behaviours evolve is a long-standing goal in evolutionary biology. Using the domestic dog as a model
-
, you have gained essentially corresponding knowledge in another way. The applicant is expected to have good knowledge of computer science, mathematics, algorithms, and programming. Knowledge and
-
to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both
-
the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics