Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Umeå University
- Uppsala universitet
- Linköpings universitet
- Linköping University
- Luleå University of Technology
- SciLifeLab
- Umeå universitet
- Chalmers University of Technology
- Linkopings universitet
- Lulea University of Technology
- Lunds universitet
- Swedish University of Agricultural Sciences
- University of Lund
- 3 more »
- « less
-
Field
-
. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum
-
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
-
interdisciplinary research on knowledge extraction from social data. Project description The project is in the emerging area of fair social network analysis. In today’s algorithmically-infused society, data about our
-
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
-
and significant piece of information to the right point of computation (or actuation) at the correct moment in time. To address this challenge, you will focus on developing theoretical and algorithmic
-
and algorithmic foundations for goal-oriented, semantics-aware communication strategies that enable efficient, intelligent, and adaptive information exchange in joint communication and control. In
-
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
-
to disease development experience in relevant evolutionary biology and phylogenetic sequence analysis publications in peer-reviewed scientific journals great importance will be placed on personal qualities
-
series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
-
that yield valid statistical conclusions (inference) on causal effects when using machine learning algorithms and big datasets. The project is part of the research environment Stat4Reg (www.stat4reg.se ), and