Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- INESC TEC
- University of Minho
- INESC ID
- Universidade de Aveiro
- Universidade de Coimbra
- FCiências.ID
- University of Algarve
- Associação Universidade-Empresa para o Desenvolvimento - TecMinho
- Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID
- Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra
- Instituto Politécnico de Coimbra
- NOVA.id.FCT- Associação para a Inovação de Desenvolvimento da FCT
- Politécnico de Leiria
- 3 more »
- « less
-
Field
-
, and eye tracker data. Work Plan: - Multimodal feature extraction from EEG, HRV, gaze dynamics, and pupil size data; - Signal fusion and model training using interpretable machine learning models (e.g
-
team. Preferential factors: academic performance, with a focus on Machine Learning and Biomedical sciences previous experience (e.g., research, professional, lecturing) in the domains of the grant
-
8 Oct 2025 Job Information Organisation/Company INESC ID Research Field Engineering » Biomedical engineering Engineering » Computer engineering Researcher Profile First Stage Researcher (R1
-
the field of the seismic behaviour of masonry structures and machine learning; Have a good proficiency of the English language. At the time of the respective hiring, candidates must prove that
-
.; - Develop skills in artificial intelligence and machine learning techniques for analyzing operational data and detecting anomalies, using foundational model approaches (e.g., GridFM project, LF Energy
-
Requirements: Applicants must be enrolled in a Master’s Degree in Computer Engineering or related fields. Proof of enrolment must be provided by the time of contracting. However, candidates may initially submit
-
experience in the fields of HRI, robotics, computer vision, or machine learning. Programming skills. Contracting requirements: Presentation of the academic qualifications and/or diplomas, if applicable
-
with a focus on traditional machine learning (shallow learning) and deep learning methodologies. Knowledge of Data Science, including the development of data analysis and visualisation pipelines. 5
-
developing statistical and machine learning approaches for the integration of cancer multi-omics data and the analysis of CRISPR-based screens. Responsibilities include designing bioinformatics workflows
-
, algorithms with a focus on traditional machine learning (shallow learning) and deep learning methodologies. Knowledge of Data Science, including the development of data analysis and visualisation pipelines. 5