Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- University of Oslo
- Nanyang Technological University
- City of Hope
- University of Arkansas
- INESC TEC
- Dana-Farber Cancer Institute (DFCI)
- European Space Agency
- Florida Atlantic University
- Monash University
- Tokyo University of Science
- Universidade de Coimbra
- University of British Columbia
- University of Nottingham
- Zintellect
- Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID
- CIC bioGUNE
- Centro de Engenharia Biológica da Universidade do Minho
- Cori Institute of Molecular and Computational Metabolism
- Cornell University
- FCiências.ID
- INESC ID
- Indiana University
- Institute of Agrochemistry and Food Technology (IATA-CSIC)
- Institute of Cosmos Sciences of University of Barcelona
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- Johns Hopkins University
- Lawrence Berkeley National Laboratory
- Life and Health Sciences Research Institute (ICVS), from the School of Medicine (EM) of the University of Minho
- Max-Planck-Institut für Bildungsforschung
- NTNU - Norwegian University of Science and Technology
- National University of Singapore
- Northeastern University
- Northwestern University
- Research Center for Molecular Medicine (CeMM), ÖAW
- SUNY University at Buffalo
- School of Sciences
- Tampere University
- Technical University of Munich
- The University of Southampton
- UCL;
- UNIVERSITY OF NOTTINGHAM NINGBO CHINA
- UiT The Arctic University of Norway
- Universidade Católica Portuguesa - Porto
- University College London
- University of Algarve
- University of Bergen
- University of California
- University of California, San Diego
- University of Idaho
- University of Leeds
- University of Leeds;
- University of Michigan
- University of New South Wales
- University of North Carolina at Chapel Hill
- University of South-Eastern Norway
- University of Texas at Austin
- Western Norway University of Applied Sciences
- 47 more »
- « less
-
Field
-
of existing studies to promote the use of risk-informed decision frameworks, prediction models, AI applied to planetary protection. Tasks include: Support the creation of probabilistic models for planetary
-
well as resource limitations. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning
-
platforms, environmental prediction models, and visualization tools as needed. Additional tasks include field experiments to test the instruments and validate models; preparing data reports and presentations
-
the Department of Geosciences. PHAB’s main goal, based on detailed studies of Earth and the solar system, is developing predictive models to identify habitable planets around other stars. Within three
-
and limitations arising from the use of AI-based methods in predictive feedback. The successful candidate will: explore how a combination of multimodal observation (audio, video, LIDAR, thermal vision
-
dynamic and uncertain environments. While Artificial Intelligence (AI) optimizes predictions or policies, energy systems are inherently multi-agent, strategic, and resource-constrained. Each agent has its
-
simulations of compact binaries (including, for example, binary black holes, binary neutron stars, and black hole–neutron star binaries). The broader goals are to generate accurate predictions for gravitational
-
help industrial firms use operational data in new ways, for example to improve fuel efficiency, support predictive maintenance, enhance safety, reduce emissions and optimize the use of resources. In
-
involved in the bioinformatic and analytic aspects of predictive modeling. MINIMUM JOB QUALIFICATIONS: A Ph.D. in bioinformatics, genetics, statistics, mathematical, physical, or computer science
-
of limited temporal and spatial accuracy of such remote interactions. We pay particular attention to the exploration of potential and limitations arising from the use of AI-based methods in predictive feedback