Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- Imperial College London
- ;
- Nature Careers
- University of Sheffield
- University of Glasgow
- Cardiff University
- City of Hope
- Erasmus University Rotterdam
- Forschungszentrum Jülich
- Freenome
- Ludwig-Maximilians-Universität München •
- Nanyang Technological University
- Stanford University
- Technical University of Denmark
- University of Bristol
- Virginia Tech
- 6 more »
- « less
-
Field
-
fellowships have the aim of identifying excellent researchers and accelerating them in using AI to advance and disrupt Science or Engineering. Here ‘AI’ is interpreted very broadly, e.g.: topics in Bayesian
-
fellowships have the aim of identifying excellent researchers and accelerating them in using AI to advance and disrupt Science or Engineering. Here ‘AI’ is interpreted very broadly, e.g.: topics in Bayesian
-
expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
-
project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
-
and reduction Development and application of big data analytics for large X-ray data sets Application of Bayesian methods to X-ray data Combinatorial analysis of various data from complementary
-
presentations, response to therapy, disease progression and complication, and that further subclassification of diabetes into more homogeneous groups offers opportunities for tailored and targeted early treatment
-
main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
-
campaigns including programmed screening or Bayesian optimisation. You will characterise the resulting materials, in terms of their properties and performance for an intended application. Sustainability will
-
create a closed loop pipeline able to rapidly design binders to any target and optimized for developability. The program is rooted in DALSA (DTU’s Arena for Life Science Automation), a new
-
candidate, you will: Develop and apply Bayesian Network machine learning methods to analyze the dynamics of G-protein coupled receptors to uncover allosteric regulation that enables design of allosteric