137 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions in Portugal
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- INESC TEC
- Universidade de Coimbra
- Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID
- University of Minho
- FEUP
- Instituto de Telecomunicações
- INESC ID
- Universidade de Aveiro
- LNEC, I.P.
- Universidade do Minho - ISISE
- University of Beira Interior
- Associação Fraunhofer Portugal Research
- Centro de Computação Grafica
- Escola Superior de Design, Gestão e Tecnologias da Produção de Aveiro - Norte da Universidade de Aveiro
- Instituto Politécnico de Coimbra
- Instituto Politécnico de Setúbal
- Instituto Superior de Engenharia do Porto
- NOVA.id.FCT- Associação para a Inovação de Desenvolvimento da FCT
- UNIVERSIDADE DE ÉVORA
- University of Algarve
- University of Trás-os-Montes and Alto Douro
- Associação COLAB TRIALS - Laboratório Colaborativo para a Inovação em Ensaios Clínicos
- Aveiro University
- CIIMAR - Interdisciplinary Center of Marine and Environmental Research - Uporto
- ESS - Escola Superior de Saúde
- FARM-ID - Associação da Faculdade de Farmácia para a Investigação e Desenvolvimento
- Faculdade de Ciências Médicas|NOVA Medical School da Universidade NOVA de Lisboa.
- Faculdade de Ciências e Tecnologia
- Fastprinciple,lda
- Gulbenkian Institute for Molecular Medicine
- ISCTE - Instituto Universitário de Lisboa
- ISCTE-IUL
- Instituto Politécnico de Bragança
- International Iberian Nanotechnology Laboratory (INL)
- LIP - Laboratório de Instrumentação e Física Experimental de Partículas
- NOVA Information Management School (NOVA IMS)
- UNINOVA - Instituto de Desenvolvimento de Novas Tecnologias
- Universidade Autónoma de Lisboa
- Universidade Católica Portuguesa - Porto
- Universidade Lusófona´s Research Center for Digital Human-Environment Interaction Lab
- Universidade da Madeira
- Universidade de Trás-os-Montes e Alto Douro
- Universidade do Algarve
- University of Aveiro
- iBET - Instituto de Biologia Experimental e Tecnológica
- 35 more »
- « less
-
Field
-
of this project for a one-year period (100% full-time commitment) to make a significant contribution to the implementation of machine learning (ML) algorithms. The postdoc is expected to have proven experience in
-
river valleys); • Identify stylistic patterns and regional variations in schematic rock art; • Apply machine learning tools for large-scale stylistic classification; • Establish a robust chronological
-
Python for scientific computing – experience with data analysis and basic signal processing – foundations in machine learning and interest in developing advanced AI models – familiarity with Linux
-
the European Research Council - ERC COG 101088763. The work for this position is in the area of Machine Learning and Natural Language Processing. We are offering We offer a challenging position with the
-
of the state of the art in machine learning for generation of artificial data; - identify and select the appropriate methods for the study in question; - develop the research capacity through the application
-
using machine learning; Interpretation of soil profiles and moisture maps. Development and validation of digital tools: Support in building georeferenced web interfaces for data visualization
-
Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 26 days ago
skills. Have very good knowledge of machine-learning and data science methods, especially for timeseries data Have very good programming skills in programming languages such as Python. Have previous
-
of scientific computing involving machine learning models for viscoelastic fluid flows. Legislation and regulations: Law Nº. 40/2004, of 18th August, in its current wording (Statutes of Scientific Research Fellow
-
for applications for one research grant within the framework of project ISA4RL - Integrating Instance Space Analysis with Auto-Reinforcement Learning for Adaptive Algorithm Selection and Configuration
-
of the Grant are:; 1) To apply machine learning algorithms for the diagnosis of faults and malfunctions in photovoltaic plants, using data from SCADA systems combined with synthetic data from digital twins (DT