Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- University of Oslo
- Nanyang Technological University
- Harvard University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- National University of Singapore
- Zintellect
- Nature Careers
- Universidade de Coimbra
- University of British Columbia
- INESC ID
- UiT The Arctic University of Norway
- Indiana University
- University of Bergen
- University of Michigan
- Cornell University
- OsloMet
- University of Birmingham
- University of Nottingham
- University of Stavanger
- Carnegie Mellon University
- Center for Drug Evaluation and Research (CDER)
- NTNU - Norwegian University of Science and Technology
- RMIT UNIVERSITY
- UCL;
- University of Texas at Austin
- Hong Kong Polytechnic University
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial
- Johns Hopkins University
- King Abdullah University of Science and Technology
- LINGNAN UNIVERSITY
- LNEC, I.P.
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Macquarie University
- Northeastern University
- The University of Queensland
- Trinity College Dublin
- UNIVERSITY OF SOUTHAMPTON
- University of London
- ;
- CSIRO
- Centro de Engenharia Biológica da Universidade do Minho
- City of Hope
- Cranfield University
- Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S)
- Instituto de Telecomunicações
- Monash University
- NTNU Norwegian University of Science and Technology
- New York University
- Princeton University
- Queen's University Belfast;
- Singapore University of Technology & Design
- The University of Memphis
- UNIVERSITY OF SURREY
- University of Leeds
- University of Lund
- University of Minho
- University of North Carolina at Charlotte
- University of South Carolina
- University of South-Eastern Norway
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Aarhus University
- AbbVie
- Academia Sinica
- American University
- Amgen
- Barnard College
- CMI - Chr. Michelsen Institute
- CRANFIELD UNIVERSITY
- Center for Biologics Evaluation and Research (CBER)
- Center for Devices and Radiological Health (CDRH)
- Central Michigan University
- Chippewa Valley Technical College
- Dana-Farber Cancer Institute
- Duke University
- Emory University
- European Space Agency
- FCiências.ID
- Florida Atlantic University
- George Mason University
- Hobart and William Smith Colleges
- Humboldt-Universität zu Berlin
- INESC TEC
- Institute of Computer Science CAS
- Instituto Pedro Nunes
- Instituto Superior Técnico
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- James Cook University
- KEK
- King's College London
- King's College London;
- Lawrence Berkeley National Laboratory
- London School of Hygiene & Tropical Medicine;
- Lunds universitet
- Manchester Metropolitan University
- Montana State University
- Nansen Environmental and Remote Sensing Center
- National Laboratory of Energy and Geology
- OCAD University
- Politécnico de Leiria
- Polytechnic University of Leiria
- 90 more »
- « less
-
Field
-
., atomate2, AFLOW). Extensive knowledge of graph-based machine learning models for interatomic potentials, along with experience in generative models for the inverse design of inorganic materials. Proficiency
-
of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical systems. Key Responsibilities Derive and analyse closed-form mathematical
-
fields. You will be an integral member of an inter-disciplinary Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical
-
models, focusing on industrial image analysis Develop advanced deep learning methods for power battery inspection models Design and implement novel algorithms for AI-based CT imaging Lead experimentation
-
research projects will be considered.) Technical expertise in machine learning and model fine-tuning – 10% Demonstrated experience with neural network training, loss function design, embedding-based models
-
PhD qualification degree in Electronic Engineering or Computer Science Familiarity with pinching antennas and machine learning Good written and oral communication skills Proficiency in python
-
. The postdoctoral fellow will lead efforts to develop novel machine learning models for integrating omics datasets (e.g., genomic, transcriptomic, epigenomic, proteomic, metabolomic) with relevant molecular pathways
-
Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical learning difficulties in kindergarten and early primary level students
-
generated quickly and regularly. Help develop machine learning techniques for feral swine abundance in data sparse environments. Collaborate with APHIS Wildlife Services (WS) to integrate data and model
-
(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP