Sort by
Refine Your Search
-
Country
-
Employer
- ETH Zurich
- European Space Agency
- Oak Ridge National Laboratory
- University of Colorado
- The University of Chicago
- McGill University
- Blekinge Institute of Technology
- Cornell University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Harvard University
- Imperial College London
- Malmö University
- Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH
- University of Delaware
- University of Glasgow
- University of Louisville
- University of Lund
- University of Michigan
- University of Southern California
- University of Washington
- ;
- AALTO UNIVERSITY
- Aalborg University
- Aix-Marseille Université
- Alma Mater Studiorum - Universita' di Bologna
- Beijing Normal-Hong Kong Baptist University
- CSIRO
- Coast Community College District
- Duke University
- EPFL
- Fraunhofer-Gesellschaft
- George Mason University
- Institute of Biochemistry and Biophysics Polish Academy of Sciences
- KTH Royal Institute of Technology
- Monash University
- NEW YORK UNIVERSITY ABU DHABI
- NIST
- Nantes Université
- Rutgers
- Rutgers University
- The California State University
- University of California
- University of California Los Angeles
- University of Canterbury
- University of Michigan - Ann Arbor
- University of Minnesota
- University of Nevada Las Vegas
- University of Sheffield
- University of Texas at Austin
- University of Tübingen
- Villanova University
- 41 more »
- « less
-
Field
-
Qualifications Experience: Relevant programming experience developing, implementing, debugging, and maintaining applications with Python. Experience working with high performance computers (e.g., parallelizing and
-
destination. Job Summary The Data Science Institute (DSI) Tech staff is responsible for the design, operation, and security of all computing infrastructure within the Data Science Institute, including research
-
exploring them. Basic data preprocessing, feature engineering, and model evaluation, or a strong willingness to gain hands-on experience. Eagerness to learn HPC concepts, including parallel computing
-
flexible unequal-variance model in a hierarchical Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques, prior distributions and
-
community composition, as well as community function, to be investigated in parallel. Standard geochemical analyses will be simultaneously performed to investigate interactions between the major nutrient