Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Nature Careers
- University of Luxembourg
- FCiências.ID
- University of Oslo
- Cornell University
- Duke University
- OsloMet – Oslo Metropolitan University
- SciLifeLab
- Technical University of Denmark
- University of Bergen
- University of Bucharest
- University of Kansas Medical Center
- University of Lund
- University of Massachusetts
- University of Tübingen
- 5 more »
- « less
-
Field
-
position as a senior researcher if given a positive academic assessment at the end of the tenure track process. The position aims to develop new approaches, methods, databases and tools to assess and
-
of aging and lifespan development Interest in studies of ambulatory assessment Good knowledge of and interest in quantitative methods (e.g., multivariate analyses, longitudinal analyses, multilevel analyses
-
, and communicate with external stakeholders. You are motivated to fully engage in research that is formally owned by others. Practical experience in the development of biochemical methods, cell culture
-
formally conferred; the candidate may present evidence of completion of the degree requirements, together with a statement documenting the date on which the degree is to be conferred. Be Bold The incumbent
-
transcripts A 3-page research proposal that presents your research project for the PhD: your research question, a short literature review, an overview over the methods and data that you aim to use The names
-
Infection Biology at the Department of Biology. Qualification requirements Applicants must have: A PhD in ecology or another relevant field Very good oral and written proficiency in English Formal training in
-
. 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
-
integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
-
integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
-
in several of the following key topics: Energy System Technologies Digital Twin Platforms and Data Analytics Predictive Models & Explainable AI (XAI) Methods in Time Series Forecasting Data Retrieval