Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
sampling in urban and rural areas; (ii) Develop/optimize/validate analytical methods to quantify MPs/PAHs in air, vegetation, and soil; (iii) Identify the impact of the 2017 forest fires in Portugal
-
samples. The candidate will be involved in developing, optimizing, and validating advanced analytical methodologies, applying them to analyze environmental samples, processing and interpreting data using
-
and Fusion A3. Data Persistence, Security, and Integration A4. Analytical Models A5. Monitoring and Services A8. Dissemination: Demonstration, Promotion, and Outreach Applicable legislation and
-
the candidate are as follows: i) To study the quality of wastewater to ensure its compliance with agricultural standards for irrigation purposes (Activity 1), using appropriate analytical methods to determine
-
- Method selection and route modelling; T10 – Data Analytics; T14 - Improve Public Perception on Raw Materials Industry; T15 - Dissemination of project results, with greater emphasis on tasks T4, T5, T6 and
-
for 1 research grant within the framework of project “Understanding Machine Learning Systems”, financed by Faculdade de Engenharia da Universidade do Porto, under the following conditions: Scientific Area
-
) First author of 5 papers published in English in journals indexed in Journal Citation Reports (JCR), reporting numerical simulation studies and/or analytic solutions of transport phenomena. b) Candidates
-
in the project proposal for Profile 7, in particular: Task3: Multimodal Data Analysis and Machine Learning; Task4: Coating Optimization and task: Dissemination. The work will focus on the study and
-
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
-
Engineering or Industrial Engineering and Management) - 10 points; Others Masters – 2 points) b) Experience in applying machine learning algorithms, data preparation, normalization, feature selection, and