Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- NEW YORK UNIVERSITY ABU DHABI
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Vanderbilt University
- University of Washington
- AALTO UNIVERSITY
- Aalborg University
- Chalmers tekniska högskola
- European Space Agency
- Duke University
- Inria, the French national research institute for the digital sciences
- Stanford University
- Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH
- University of Kentucky
- University of Minnesota
- University of Texas at Dallas
- Washington University in St. Louis
- ;
- Boston University
- Chalmers University of Technology
- City University London
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Published yesterday
- Empa
- FAPESP - São Paulo Research Foundation
- Illinois Institute of Technology
- Kennesaw State University
- MUNSTER TECHNOLOGICAL UNIVERSITY
- Maastricht University (UM); yesterday published
- National Aeronautics and Space Administration (NASA)
- Nature Careers
- New York University
- Pennsylvania State University
- Rutgers University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- THE UNIVERSITY OF HONG KONG
- Tampere University
- Technical University of Denmark
- Tilburg University
- UNIVERSITY OF SYDNEY
- University College Dublin
- University Medical Center Utrecht (UMC Utrecht)
- University of Amsterdam (UvA)
- University of Bergen
- University of California, Berkeley
- University of Lille
- University of Nevada, Reno
- University of New Hampshire
- University of Oulu
- University of South Carolina
- University of Southern Denmark
- University of Sydney
- University of Twente (UT)
- Uppsala universitet
- Virginia Tech
- 45 more »
- « less
-
Field
-
: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
-
an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and agricultural datasets proficiency in R and/or
-
agricultural science with a quantitative focus (or an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and
-
Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
-
learning with synthetic or simulated data. Developing and analyzing new algorithms for AI calibration, run-time reliability monitoring, and adaptive decision-making in wireless environments. Collaborating
-
and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. About the research project The postdoc will work at Chalmers University of Technology in a
-
Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
-
on the plants Arabidopsis thaliana will generate maps of depolarization, retardance, dichroism, and optical axis azimuth, which will feed machine learning models developed by the project partners to identify
-
techniques (FTIR, NIR, Raman, …) with machine learning/ chemometrics. Responsibilities of the Position The Postdoctoral researcher is intended to support the soil spectroscopy research activities and digital
-
, atmospheric signals), data fusion across sensing modalities, and development of scalable machine learning pipelines. Work will be entirely computational and based in Seattle, with no field deployment