Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nature Careers
- DAAD
- NTNU - Norwegian University of Science and Technology
- Chalmers University of Technology
- Technical University of Denmark
- Forschungszentrum Jülich
- Cranfield University
- Ghent University
- SciLifeLab
- Technical University of Munich
- ; The University of Manchester
- ; University of Warwick
- Abertay University
- University of Groningen
- University of Southern Denmark
- Utrecht University
- ;
- ; Swansea University
- ; University of Sheffield
- Curtin University
- KNAW
- Leibniz
- University of Adelaide
- University of Bremen •
- University of Copenhagen
- University of Twente
- Universität Hamburg •
- ; Aston 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
- Colorado State University
- Copenhagen Business School , CBS
- Empa
- Fraunhofer-Gesellschaft
- Heidelberg University
- Heidelberg University •
- Justus Liebig University Giessen •
- Karlsruhe Institute of Technology •
- 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 •
- WIAS Berlin
- Wageningen University and Research Center
- 55 more »
- « less
-
Field
-
of data-driven approaches within these multi-parameter models to produce faster and more robust correlations and tools that can be incorporated within industrial methods and have an impact on future designs
-
will lead to natural collaboration opportunities. The primary methods used in this project will be experimental, involving fluid characterisation and high-speed imaging experiments, using Phantom high
-
monitoring will be based on real time data streaming from the machine numerical control. The project will cover all the aspects related to the implementation and automation of the tool life cycle management
-
. The link between graphite crystallinity, flake size, and purity (all being important for industrial applications) and its formation history is not fully understood. Therefore, the numerous graphite
-
and very good knowledge in quantitative and qualitative research methods Good knowledge of statistical software (e.g. SPSS or STATA or R or JASP) Strong commitment and the ability to work in a team
-
Master in Experimental Psychology, HCI, UX or related fields Experience with research methods in social sciences (e.g., empirical research, experiment design) Experience in statistical methods is
-
technology, which works by cutting a DNA sequence at a specific genome location and deleting or inserting genes there. The CRISPR-Cas method provides an excellent basis for the development of unprecedented
-
computing Advanced knowledge of numerical methods Geophysical fieldwork experience, preferably with GPR, EMI and ERT Strong English writing skills Since the work involves interdisciplinary cooperation with
-
: Mathematics, Mathematical Statistics and Computational Mathematics. The research at the Division of Computational Mathematics covers many different areas in numerical analysis, symbolic computations
-
utilise numerical techniques including the finite element method to describe biofluid flow and deformation in the human brain tissue. Parameters are inferred from clinical data including medical images