Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- University of Oslo
- Zintellect
- Nanyang Technological University
- King's College London
- University of California
- University of Michigan
- City of Hope
- Dana-Farber Cancer Institute (DFCI)
- Francis Crick Institute
- INESC TEC
- Lawrence Berkeley National Laboratory
- University of Bergen
- ADELAIDE UNIVERSITY
- CRANFIELD UNIVERSITY
- Center for Drug Evaluation and Research (CDER)
- Cranfield University
- Dartmouth College
- Georgia Southern University
- Hong Kong Polytechnic University
- Indiana University
- Kingston University
- Macquarie University
- NTNU - Norwegian University of Science and Technology
- Naturalis
- Northeastern University
- Oslo University Hospital
- Queen's University Belfast
- RMIT UNIVERSITY
- RMIT University
- School of Sciences
- SciLifeLab
- Simons Collaboration on the Physics of Learning and Neural Computation
- Simons Foundation/Flatiron Institute
- Tampere University
- UNIVERSITY OF SYDNEY
- University of Birmingham
- University of Cambridge;
- University of Guelph
- University of Hertfordshire;
- University of Melbourne
- University of Nottingham
- University of Sussex;
- University of Sydney
- University of Texas at Austin
- 34 more »
- « less
-
Field
-
expensive and dangerous health threats, and responds when these arise. Research Project: You will gain training and experience in the analysis of large population-based and healthcare databases within
-
analytical skills in SAS, SUDAAN, Stata, R, Python, or related data manipulation and analysis software Experience analyzing complex surveys, developing cohort analyses, and testing health outcomes in cross
-
for medical imaging, precision medicine, statistical genomics, graphical models, and the analysis and integration of complex, high-dimensional biomedical data. The fellow will be expected to develop novel
-
(ML), to help solve complex agricultural problems that also depend on collaboration across scientific disciplines and geographic locations. In addition, many of these technologies rely on the synthesis
-
in machine learning, systems modeling, optimization, or analysis of complex biological and clinical data. Demonstrated proficiency in programming languages such as Python and/or R, with experience
-
, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural
-
the genetic basis of complex diseases. Our research sits at the intersection of population genetics, statistical genetics, and evolutionary disease genetics, with a particular emphasis on leveraging
-
evaluation, performance measurement, portfolio analysis, evidence-informed decision support, enterprise-level public health planning, multidisciplinary collaboration, and the translation of complex technical
-
Analysis of Economic Impacts of Ultra-Processed Foods to lead quantitative research on the economic impacts of ultra-processed foods, managing and analyzing large, complex datasets to generate robust, policy
-
29 Jul 2026 Job Information Organisation/Company UNIVERSITY OF SYDNEY Research Field Computer science Economics Mathematics Environmental science Geosciences Researcher Profile Recognised Researcher