Sort by
Refine Your Search
-
Country
-
Employer
- Zintellect
- University of Oslo
- Center for Devices and Radiological Health (CDRH)
- King's College London
- Nanyang Technological University
- University of Bergen
- University of British Columbia
- University of California
- Dana-Farber Cancer Institute (DFCI)
- Harvard University
- University of Agder
- CIC bioGUNE
- Centro de Engenharia Biológica da Universidade do Minho
- City of Hope
- FEUP
- Francis Crick Institute
- Gulbenkian Institute for Molecular Medicine
- Institute of Cosmos Sciences of University of Barcelona
- Instituto de Educação da Universidade de Lisboa
- Lawrence Berkeley National Laboratory
- Max Planck Institute of Biochemistry, Martinsried
- Max-Planck-Institut für Bildungsforschung
- NTNU - Norwegian University of Science and Technology
- National University of Singapore
- RMIT University
- Tampere University
- The Francis Crick Institute
- The University of Queensland
- The University of Tokyo
- University of Algarve
- University of Idaho
- University of New South Wales
- University of South-Eastern Norway
- Western Norway University of Applied Sciences
- 24 more »
- « less
-
Field
-
clinical data for prediction and biological discovery Prospective clinical sequencing to guide the care of cancer patients Studies of coding, non-coding, RNA, and spatial-based drivers of cancer development
-
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
-
questions. Implements and adapts machine learning and AI-based approaches for high-dimensional epidemiological data, including variable selection, prediction modelling, and data integration. Retrieves
-
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
-
, e.g. HPLC, ICP-MS. Experience with using nuclear-reaction codes which are commonly used to predict the reaction cross sections for medical isotope production, e.g. TALYS, TENDL, CoH, ALICE and EMPIRE
-
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
-
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
-
theoretical prediction. Develop a new understanding of the fundamental flow physics through theoretical and/or numerical work. Develop independence and be self-driven to advance with their research project
-
. The aim is to develop and analyze advanced models that integrate heterogeneous maritime data sources - such as AIS, metocean, emissions, port, cargo, and business data - to improve predictions of costs
-
Organization U.S. Department of Defense (DOD) Reference Code AFIT-2026-0010 How to Apply Click on Apply at the bottom of the opportunity to start your application. Description The Air Force