Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- Imperial College London
- Princeton University
- SciLifeLab
- University of Groningen
- University of Oslo
- ;
- ; University of Oxford
- CEA
- Centre for Genomic Regulation
- City University of Hong Kong
- Curtin University
- European Space Agency
- Florida Atlantic University
- Fraunhofer-Gesellschaft
- Johns Hopkins University
- King Abdullah University of Science and Technology
- Marquette University
- Nature Careers
- RMIT University
- The Ohio State University
- University of Adelaide
- University of Kansas
- University of London
- University of Sheffield
- University of South Carolina
- VIB
- 16 more »
- « less
-
Field
-
achieved by combining state-of-the-art algorithms from multiple domains such as evolutionary algorithms, reinforcement learning, and control theory. The main responsibility of the successful applicant will
-
achieved by combining state-of-the-art algorithms from multiple domains such as evolutionary algorithms, reinforcement learning, and control theory. The main responsibility of the successful applicant will
-
to accelerate evaluation of costly simulations Genetic algorithms and other evolutionary techniques to generate a diverse set of high-performing solutions. You will design and implement new optimization
-
both the algorithms for robot co-design, and the real-world evaluation of the designs that emerge. You will investigate evolutionary algorithms to explore creative new hand designs, and reinforcement
-
minimizing error and maximizing efficiency, is computationally challenging—no known polynomial-time algorithm exists to solve it optimally in all cases. Because of this complexity, researchers typically rely
-
HiPerBreedSim project. In this role, you will leverage recent advances in working with ancestral recombination graphs (ARGs) to develop algorithms and code for simulating population genomic data, including
-
, evolutionary theory, quantitative genetics, data science, toxicology, and law. Within this project, our group at the CRG focuses on the analysis of bulk and single cell RNA-seq from five different model species
-
, tailored, complex materials such as advanced polymers or polymeric formulations, catalysts, mechatronic devices and software and algorithms. Design, control and modelling and analyses complement
-
needs, such as assisting the team with evaluating evolutionary algorithms for exploring creative new hand designs, or reinforcement learning for policy optimisation, all within a huge GPU-based simulation
-
challenges in neuroscience, healthcare, and computing; and developing machine learning algorithms to analyze large experimental datasets that deepen our understanding of information processing in the brain