Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- INESC TEC
- Universidade de Coimbra
- University of Porto
- NOVA.id.FCT- Associação para a Inovação de Desenvolvimento da FCT
- INESC ID
- Faculdade de Ciências e Tecnologia
- ISCTE - Instituto Universitário de Lisboa
- Centro de Computação Grafica
- FEUP
- INESC COIMBRA - INSTITUTO DE ENGENHARIA DE SISTEMAS E COMPUTADORES DE COIMBRA
- Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra
- Instituto Politécnico de Beja
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial
- Instituto de Telecomunicações
- Politécnico de Leiria
- RAEGE-Az
- Universidade Nova de Lisboa
- Universidade do Minho - ISISE
- University of Trás-os-Montes and Alto Douro
- iBET - Instituto de Biologia Experimental e Tecnológica
- 10 more »
- « less
-
Field
-
to or higher than 16 points. 4. Work Plan: 4.1. The purpose of this contract is to perform the following tasks: Literature review in artificial intelligence algorithms for Dataset Curation; Development
-
requirements described will be excluded. Preferential factors: Experience in image analysis and processing using AI algorithms; English language proficiency; Portuguese language fluency. APPLICATION
-
the execution of the following tasks related to modelling the incineration process of the pilot units, with the following main objectives: - development of evolving algorithms based on Machine Learning
-
studies, technical specifications, modelling and system design. The work to be developed involves continuous analysis of the state of the art, the definition and specification of technical and functional
-
activities will be developed in the Power Electronics area, focusing on programming control algorithms on a Xilinx FPGA. This project aims to implement internal fault tolerance in a power electronics
-
to or higher than 16 points. 4. Work Plan: 4.1. The purpose of this contract is to perform the following tasks: Literature review in artificial intelligence algorithms for multi target contexts; Development
-
. Implementation of signal detection algorithms and triangulation ; 4. Planning and participating in field tests to evaluate system performance; 5. Reporting and disseminating the work developed (ideally with a
-
solution will utilize information collected through specific sensors installed in the infrastructure, which will be processed by advanced Artificial Intelligence algorithms. The works listed for this grant
-
approaches, including machine learning algorithms, for analyzing biological data. Hands-on experience in programming environments like Bash, R, or Python. Solid interpersonal and communication skills and
-
learning methods to digital pathology Development of deep learning algorithms for the computational analysis of whole-slide images. The objective is to identify relevant biological features and to perform