Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nature Careers
- DAAD
- Chalmers University of Technology
- NTNU - Norwegian University of Science and Technology
- Forschungszentrum Jülich
- Ghent University
- Technical University of Denmark
- ; The University of Manchester
- ; University of Warwick
- Cranfield University
- SciLifeLab
- Technical University of Munich
- University of Groningen
- Abertay University
- University of Southern Denmark
- Utrecht University
- ;
- ; University of Sheffield
- Curtin University
- Heidelberg University
- KNAW
- University of Adelaide
- University of Bremen •
- University of Copenhagen
- University of Twente
- Universität Hamburg •
- ; Loughborough University
- ; Manchester Metropolitan University
- ; Swansea University
- ; Technical University of Denmark
- ; University of East Anglia
- ; University of Leeds
- ; University of Reading
- ; University of Southampton
- ; University of Surrey
- ; University of Sussex
- Arizona State University
- CWI
- Colorado State University
- Copenhagen Business School , CBS
- Empa
- Fraunhofer-Gesellschaft
- Heidelberg University •
- Justus Liebig University Giessen •
- Karlsruhe Institute of Technology •
- Leibniz
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Monash University
- Norwegian Meteorological Institute
- Saarland University •
- Swinburne University of Technology
- TU Bergakademie Freiberg
- Trinity College Dublin
- UiT The Arctic University of Norway
- Umeå University
- University of Bonn •
- University of British Columbia
- University of Göttingen •
- University of Konstanz •
- University of Münster •
- University of Nebraska–Lincoln
- University of Newcastle
- University of Nottingham
- University of Oslo
- University of Stuttgart •
- Wageningen University and Research Center
- 56 more »
- « less
-
Field
-
datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning). The goals are to develop new computational methods that allow the scientific inference
-
. To do this, knowledge or willingness to be trained in advanced statistical modelling, ideally with an interest in methods for causal inference in observational data, is strongly preferred. Using various
-
department collaborates with numerous national and international partners and with local clinical and research departments. We offer a dynamic, interdisciplinary environment with extensive training
-
thorough understanding of theoretical/numerical methods for simulating optical phenomena, experience in fabrication and/or characterization of micro- or nanostructures, hands-on experience with fiber-optic
-
. The successful candidate will work on developing new theoretical models and computational methods to investigate the fundamental limits of polariton-assisted inelastic electron tunneling in tunnel junctions made
-
of ambulatory assessment and very old age Good interpersonal skills and interest of working with older adults Good knowledge of and interest in longitudinal quantitative methods (e.g., multivariate analyses
-
sources. Such modelling provides essential tools for designing low-carbon, efficient, and adaptive energy infrastructures at local level. By leveraging data-driven methods, cities can devise strategies
-
, efficient, and adaptive energy infrastructures at local level. By leveraging data-driven methods, cities can devise strategies that balance energy supply and demand, improve electricity grid flexibility, and
-
identifying which patients will benefit from surgical valve repair. To address these issues, better patient selection methods and deeper insights into disease mechanisms are needed. This project proposes
-
advanced simulation methods, including Reynolds-Averaged Navier-Stokes (RANS), Direct Numerical Simulations (DNS), and/or Large Eddy Simulations (LES), will be employed to accurately model the complex flow