Sort by
Refine Your Search
-
Category
-
Country
-
Program
-
Employer
- Imperial College London
- ;
- Nature Careers
- University of Sheffield
- Forschungszentrum Jülich
- University of Glasgow
- City of Hope
- Erasmus University Rotterdam
- Freenome
- Ludwig-Maximilians-Universität München •
- Nanyang Technological University
- RMIT University
- SciLifeLab
- Stanford University
- Technical University of Denmark
- The University of Arizona
- University of Bristol
- University of South-Eastern Norway
- University of Toronto
- Virginia Tech
- 10 more »
- « less
-
Field
-
have recently developed a new method of statistical analysis for the problem of finding the key genes for disease, with exciting pilot results and promising drug targets for rheumatoid arthritis and
-
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
-
strategies. Your tasks in detail: Enhance existing Bayesian state estimation with reliability margins using both simulated and, if necessary, real-world grid data. Develop Use-Case-Specific Reinforcement
-
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
-
methodologies. Understanding of integrating Bayesian approaches in NN-based model Knowledge of model deployments to cloud platforms or past work with AutoML tools. Knowledge of MLFlow for maintaining model
-
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
-
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
-
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