Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Forschungszentrum Jülich
- Tilburg University
- ;
- DURHAM UNIVERSITY
- Durham University
- LINGNAN UNIVERSITY
- National Renewable Energy Laboratory NREL
- Newcastle University
- Queensland University of Technology
- Swinburne University of Technology
- The University of Auckland
- University of Arkansas
- University of Minnesota
- University of Tennessee, Knoxville
- University of Washington
- Virginia Tech
- 6 more »
- « less
-
Field
-
this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
-
theory and methods by taking several PhD-level courses (about 36 European credits) in information systems, operations management, econometrics, machine learning, extensive data analytics and qualitative
-
Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop 3D+t
-
, econometrics, machine learning, extensive data analytics and qualitative research methods. The student will collaborate closely with advisors to identify, develop, and carry out their research project. The PhD
-
Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop
-
Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem
-
, network analysis, or machine learning are a plus Good organisational skills and ability to work both independently and collaboratively Effective communication skills and an interest in contributing to a
-
We are seeking a highly motivated individual- either a Grade 7 PhD holder, or a Grade 6 graduate - to join the EYESAVE project, funded by the Vivensa Foundation Trust, as a Patient and Public
-
on applying computer vision, machine learning, and sensor fusion to automatically detect, classify, and localize defects, improving the scalability and reliability of building inspection. Research on 3D
-
machine learning to support early detection and prioritisation of patients at risk of vision loss. The role involves leading PPIE activities to ensure that patient perspectives and lived experiences shape