RSS Feed Source: Academic Keys

Job ID: 257960

INESC TEC | Research Grant (AE2025-0242)
INESC TEC

Research Opportunities

Energy

Work description

Implementation of human-AI-digital twin interaction strategies. Explore methodologies for digital twin simulation for artificial intelligence algorithms. Prepare technical documentation in GitHub.

Academic Qualifications

Studies in electrical and computer engineering or informatics or similar.

Minimum profile required

Knowledge of Python programming.

Preference factors

Fluency in English (spoken and written). Knowledge of power systems operation. Experience in developing and testing software modules.

Maintenance stipend: € 1040.98 or 1309.64, according to the table of monthly maintenance stipend for FCT grants , paid via bank transfer. Grant holders may be awarded potential supplements, according to a quarterly evaluation process (Articles 19, 21 and 22 of the Regulations for Grants of INESC TEC and

Click this link to continue reading the article on the source website.

RSS Feed Source: Academic Keys

Job ID: 257953

INESC TEC | Research Grant (AE2025-0235)
INESC TEC

Research Opportunities

Power Systems

Work description

Manage database resources developed in the project to support effective data exchange between distributed energy assets. Keep updated the versions of the Protocol Converter of legacy systems developed in the project and update the records of data exchanges in distributed energy resources. Manage and maintain the load prediction tools for powering battery dispatch services and photovoltaic generation forecasting. Analyze battery operation and photovoltaic generation data to calculate performance indicators defined in the project. Write the scholarship activity report.

Academic Qualifications

Computer engineering, computer science, data science or similar areas.

Minimum profile required

Student of Computer Engineering, Computer Scientist, data science or similar areas. Fluency in English (written and spoken). Experience

Click this link to continue reading the article on the source website.

RSS Feed Source: Academic Keys

Research Opportunities

Programming, Computer engineering

Work description

The goal of this research is to evaluate how Large Language Models (LLMs) can be leveraged to address forecasting challenges in the energy sector, specifically those that can be framed as sequence-to-sequence problems.

The grant holder will explore different strategies on prompt engineering, compare the potential of different LLM’s and create a methodology that can be replicable to multiple challenges for the energy sector.

Academic Qualifications

Electrical Engineering, Informatics, Computer Science or similar courses.

Minimum profile required

Programming experience in Python. Basic understanding of machine learning concepts. Experience using data science tools (e.g., pandas, scikit-learn, matplotlib). Familiarity with version control systems (Git).

Preference factors

Familiarity with Large Language Models (e.g., Mistral, LLaMA). Understanding of machine learning workflows and forecasting methods. Python oriented to data manipulation and analytics.

Maintenance stipend: € 651.12, according

Click this link to continue reading the article on the source website.

RSS Feed Source: Academic Keys

Job ID: 257948

INESC TEC | Doctor Researcher (AE2025-0235)
INESC TEC

Research Opportunities

Computer Science

Work description

Contribution to the preparation of R&D proposal proposals. Development of intelligent models within the scope of electrical energy systems. Supervision of students and scholarship holders to develop work related to the area of work. Development of scientific production oriented to international journals and conferences, and participation in scientific and technological dissemination initiatives.

Academic Qualifications

Ph.D. degree in Computer Science or a related scientific field, and who possess a scientific and professional track record demonstrating a profile suitable for the activities to be carried out, are eligible to apply.

Minimum profile required

Ph.D. holders with experience in the development of data science-based solutions applied to the domain of electric power systems,

Click this link to continue reading the article on the source website.