Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- INESC TEC
- Instituto Pedro Nunes
- FCiências.ID
- 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
- Nova School of Business and Economics
- Universidade Nova de Lisboa
- Universidade de Coimbra
-
Field
-
Institute of Systems and Robotics, Faculty of Sciences and Technology of the University of Coimbra | Portugal | 3 days ago
Engineering Research Field Engineering » Electrical engineering Engineering » Electronic engineering Engineering » Mechanical engineering Engineering » Computer engineering Researcher Profile First Stage
-
the following mandatory requirements: a) A completed degree in Chemical and Biological Engineering; b) Good knowledge in the areas of Machine Learning, Microbiology, Knowledge Graphs, and Language
-
-award courses of Higher Education Institutions. Preference factors: Machine Learning Knowledge. Knowledge of signal processing and machine learning libraries (e.g., PyCaret, scikit-learn). Minimum
-
PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning for lung cancer imaging data; - identify and select the appropriate methods for the study in question; - develop
-
following mandatory requirements: a) A completed degree in Computer Engineering; b) Good knowledge in the areas of Machine Learning, Natural Language Models, and Computer Security – information to be provided
-
TRAINING: Literature review on anomaly detection in network data; Using deep learning to detect anomalies in network data flows.; 4. REQUIRED PROFILE: Admission requirements: Degree in Computer Engineering
-
domain in the design of deep learning algorithms for cardiovascular disease detection. 4. REQUIRED PROFILE: Admission requirements: Master's degree in Biomedical Engineering, Computer Engineering
-
results. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - Develop machine learning-based models from data.; - Validate the developed models with real data.; - Publicize the work in international
-
AND TRAINING: - survey and analyze the state of the art in emerging wireless networks, including simulation aspects using real data assimilation, Machine Learning, and digital twin approaches
-
with simulation techniques, energy efficiency models, large-scale energy consumption data, machine learning techniques and interpretation (unsupervised); - Education, experience and research orientation