Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Nature Careers
- AALTO UNIVERSITY
- Technical University of Denmark
- Leibniz
- Duke University
- Lund University
- Lunds universitet
- Universitaetsklinikum Erlangen
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Amsterdam
- University of Lund
- University of Sheffield
- Utrecht University
- 3 more »
- « less
-
Field
-
for the Research Assistant, Grade 6 level, position must have a BSc or MSc in a quantitative biology discipline, statistics or machine learning along with experience of research using statistical modelling
-
shape their choices. You will conduct learning and preference trials, help to design the experimental procedure, and analyse and write up the findings for academic publications. You will also be part of
-
, foraging on artificial prey, to understand both how they weight prey size and distastefulness, as well as how the available options shape their choices. You will conduct learning and preference trials, help
-
, informatics, computational sciences); at least two years working experience in the computational analysis of imaging, omics, or clinical data; strong proficiency with machine learning and statistics; strong
-
from all backgrounds to join our community. The Department of Information and Communications Engineering is now inviting applications for a Postdoctoral or Doctoral Researcher in statistical signal
-
groups working on digital health and wellbeing , network science , computational social science , and various topics in machine learning. You will be working in the research group of one of the PIs
-
, after a training period, independently analyze genomic data using machine learning and other statistical methods. All work will be carried out in a collaborative research team, requiring the sharing
-
machine learning and other statistical methods.All work will be carried out in a collaborative research team, requiring the sharing of expertise, open discussion of results, and the facilitation
-
and optimize the detailed methods to pursue the overall project goals and, after a training period, independently analyze genomic data using machine learning and other statistical methods. All work will
-
genomic summary statistics (e.g., diversity, inbreeding, mutational load) Interest in machine learning applications (experience is a plus but not required) A strong understanding of evolutionary and