Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
experience will be valued. Proven expertise and experience in computational statistics, machine learning, programming in R and/or Python, the development of methodological procedures, and the implementation
-
. Previous research experience in a relevant field will be valued. Training and/or experience in programming, data processing and analysis, and machine learning will be considered preferential factors. Taking
-
power systems, network analysis and power flow; - Experience or academic background in machine learning, Graph Neural Networks (GNN)/Grid Foundation Models and/or probabilistic methods and Monte Carlo
-
the European Union through the COMPETE 2030 Programme, of Portugal 2030, under the following conditions: Scientific Area: Machine Learning Admission requirements: Candidates who cumulatively meet the following two
-
knowledge of machine learning models and Python tools for signal processing and machine learning. General knowledge of system architecture and APIs. Previous knowledge of physiological signal processing. 5
-
biosignals. Application of machine learning techniques for classification of different classes using the extracted features. Assembly, documentation, testing, and use of an innovative biosensing system
-
models to characterize lung cancer based on a non-invasive methodology. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning
-
planning and identification of strings and modules in the field. Development of a computer vision and machine learning pipeline for the detection, localisation and classification of defects in photovoltaic
-
1 Sep 2026 Job Information Organisation/Company University of Minho Department Centro ALGORITMI Research Field Engineering » Computer engineering Researcher Profile Other Profession Positions
-
Reference Number AE2026-0230 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0230.pdf CALL FOR