Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Nature Careers
- Northeastern University
- Stony Brook University
- University of Glasgow
- University of Sheffield
- CEA
- Cardiff University
- Case Western Reserve University
- Monash University
- Nanyang Technological University
- Queensland University of Technology
- Rutgers University
- The Ohio State University
- University of California
- University of California, San Diego
- University of Oslo
- University of Toronto
- University of Washington
- Zintellect
- 9 more »
- « less
-
Field
-
to facilitate the accomplishment of biodiversity conservation research objectives. Develops and writes new proposals to secure contracts for grant-funded research related to biodiversity conservation and the use
-
meet the goals and objectives of the department and institution. Minimum Education and/or Training: Bachelor's degree in mathematics, engineering, or computer science with advanced training in
-
for: Operational research and combinatorial optimization (e.g., solvers Gurobi, CPLEX, Hexaly) Bayesian optimization, evolutionary algorithms, or hybrid methods Multi-objective and constrained optimization Surrogate
-
learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
-
with large datasets, with a minimum 200 records, using statistical software packages including SAS, R, SPSS, and STATA. (Required) Demonstrated knowledge of at minimum one general object-oriented
-
research objectives and proposals for own or joint research including research funding proposals To attend and or present at conferences/seminars at a local and national level as required To undertake
-
techniques. The incumbent is expected to exercise sound judgment in selecting and applying appropriate methods and techniques to achieve research objectives, working independently within broadly defined
-
integrate sophisticated AI systems, rigorously testing, validating, and tracking learning models, and troubleshooting issues to ensure system accuracy and reliability. A core objective of this role is to
-
methods, Bayesian statistics, and/or an interest in applied empirical problems. We are particularly interested in candidates with expertise in applications of artificial intelligence in marketing
-
Research Associate to contribute to a project focused on robust Bayesian inference with possibility theory. Robust inference is crucial for many real applications in which datasets are invariably corrupted