Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- FEUP
- Universidade de Coimbra
- Cranfield University
- Escola Superior de Design, Gestão e Tecnologias da Produção de Aveiro - Norte da Universidade de Aveiro
- Faculty of Sciences of the University of Porto
- Instituto Politécnico de Bragança
- California State University Channel Islands
- DAAD
- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- The California State University
- Universidade Católica Portuguesa - Porto
- University of Aveiro
- University of Trás-os-Montes and Alto Douro
- 3 more »
- « less
-
Field
-
Escola Superior de Design, Gestão e Tecnologias da Produção de Aveiro - Norte da Universidade de Aveiro | Portugal | about 2 months ago
Optimization of part design based on computational technologies Manufacturing of proof-of-concept parts using AI-optimized parameters for iSLS Post-processing of proof-of-concept parts Participation in
-
Escola Superior de Design, Gestão e Tecnologias da Produção de Aveiro - Norte da Universidade de Aveiro | Portugal | about 2 months ago
Regulations of the University of Aveiro. 5. Work Plan: This project aims to develop solutions based on Artificial Intelligence for optimizing additive manufacturing processes. Machine learning techniques will
-
requirements: Candidates should possess expertise in the characterization of nutrients and bioactive compounds derived from undervalued resources and food products. Experience in optimizing extraction processes
-
; consistent messaging; customer service; and processing. Extensively collaborates with FAS staff leadership on best practices, policies and business process development, optimization, maintenance, and
-
agriculture. iii) Definition of a technological maturity model to determine the most suitable digital technologies to improve and optimize olive production, including the definition of key indicators. iv
-
based on deviations from expected operating conditions or optimization of operational parameters. iv) Development of a user interface for data visualization and recommendations, allowing the user
-
-vision algorithms with edge-computing processing for the automatic detection of non-conformities. Machine-learning techniques will be applied to optimize cutting parameters, and the module will be
-
and the magnetocaloric materials; iii) producing liquid metal with high thermal conductivity and optimized rheological properties for efficient heat exchange. Additionally, the fellow will contribute
-
process development, optimization, maintenance, and communication. As a member of the leadership team, responsible for conducting the following activities on behalf of the Financial Aid function: Planning
-
analytical techniques while contributing to the optimization of toilet system performance through rigorous scientific analysis and data interpretation. Cranfield’s world-class expertise, large-scale facilities