Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Monash University
- University of Sheffield
- ;
- ETH Zurich
- Fraunhofer-Gesellschaft
- University of Glasgow
- University of New South Wales
- University of Toronto
- Forschungszentrum Jülich
- NIST
- Nature Careers
- Northeastern University
- Yale University
- BNU-HKBU United International College
- Columbia University
- Duke University
- Rice University
- SUNY Polytechnic Institute
- Stony Brook University
- University of Bristol
- University of Cambridge
- University of Houston Central Campus
- University of Lund
- University of North Carolina at Chapel Hill
- University of Nottingham
- Aston University
- Baylor College of Medicine
- Binghamton University
- Career Education Corporation
- Case Western Reserve University
- Colorado Technical University
- Erasmus University Rotterdam
- Florida International University
- Freenome
- Grand Valley State University
- Harbin Engineering University
- Harvard University
- Imperial College London
- Indian Institute of Science Education & Research Thiruvananthapuram
- Institut Pasteur
- KINGS COLLEGE LONDON
- Leibniz
- Michigan State University
- Nanyang Technological University
- Oak Ridge National Laboratory
- Queensland University of Technology
- RMIT University
- SUNY University at Buffalo
- SciLifeLab
- Simons Foundation/Flatiron Institute
- Technical University of Munich
- Texas A&M University
- The Ohio State University
- The University of Chicago
- University of Alabama, Tuscaloosa
- University of Delaware
- University of Hong Kong
- University of Nebraska–Lincoln
- University of North Carolina Wilmington
- University of Oklahoma
- University of Oregon
- University of Oslo
- University of Texas Health Science Center San Antonio
- University of Texas at Arlington
- University of Utah
- Zintellect
- 56 more »
- « less
-
Field
-
environments and inaccurate prior maps, to name a few. In order to cope with these challenges different methods will be developed. Knowledge of Bayesian methods for sensor data fusion, mapping and multiple
-
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
-
recovery trajectories and injury patterns. Integrate personalized physiological measurements into a recovery prediction model, while adapting Bayesian Neural Networks for SCI data and analyzing the impact on
-
. Required Skill/Ability 1: Demonstrated coursework in generalized linear, generalized linear mixed, survival, and Bayesian models and/or machine learning methods. Required Skill/Ability 2: Knowledge and
-
experience. Required Skill/Ability 1: Demonstrated coursework in generalized linear, generalized linear mixed, survival, and Bayesian models and/or machine learning methods. Required Skill/Ability 2: Knowledge
-
: Experience in machine learning, optimization, or AI-driven decision-making Preferably knowledge of Bayesian optimization or Gaussian processes Programming experience (Python, MATLAB, or similar) Soft Skills
-
being classed as ‘world-leading’ or ‘internationally excellent’. The highly research active SP Section comprises 13 permanent academic staff with research interests in Bayesian computational statistics
-
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
-
work in close partnership with the wet-lab team and use novel computational approaches and algorithms including A.I. and Bayesian statistical methods to infer causal relationships between mtDNA variants
-
, longitudinal data analysis, Bayesian statistics, and/or machine learning. Strong programming skills in statistical software packages such as R, SAS, or Python. Evidence of potential for excellence in teaching