Job offer

European Commission; Portugal; Presencial; EUR 30 000 - 40 000; Tempo integral

Resumo da oferta

A INESC TEC em Portugal está aceitando candidaturas para uma Bolsa de Iniciação à Pesquisa, focando na aplicação de Modelos de Linguagem Grande para previsões no setor de energia. Os candidatos devem ter formação em Engenharia, Informática ou Ciência da Computação, e experiência em Python e aprendizado de máquina é preferida.

Serviços

Seguro de saúde, suportado pela INESC TEC

Possibilidade de suplementos baseados em avaliação trimestral

Qualificações

• Experiência de programação em Python é essencial.
• Compreensão básica de conceitos de aprendizado de máquina.
• Experiência com ferramentas de ciência de dados desejada.

Responsabilidades

• Investigar aplicabilidade de Modelos de Linguagem Grande no setor de energia.
• Desenvolver protótipos para simulações com conjuntos de dados reais.
• Comparar desempenho dos LLMs com métodos tradicionais de previsão.

Conhecimentos

Programação em Python

Entendimento de aprendizado de máquina

Experiência com ferramentas de ciência de dados (pandas, scikit-learn, matplotlib)

Familiaridade com sistemas de controle de versão (Git)

Formação académica

Engenharia Elétrica

Informática

Ciência da Computação

Descrição da oferta de emprego

Organisation/Company INESC TEC Research Field Engineering » Computer engineering Computer science » Programming Researcher Profile First Stage Researcher (R1) Country Portugal Application Deadline 30 Jun 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 36 Offer Starting Date 1 Aug 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number AE2025-0233 Is the Job related to staff position within a Research Infrastructure? No

CALL FOR GRANT APPLICATIONS (AE2025-0233)

INESC TEC is now accepting grant applications to award 1 Research Initiation Grant (BII) within the scope of the ATE funded by IAPMEI with reference 56 Co-financed by Component 5 - Capitalization and Business Innovation, integrated in the Resilience Dimension of the Recovery and Resilience Plan within the scope of the Recovery and Resilience Mechanism (MRR) of the European Union (EU), framed in the Next Generation EU, for the period 2021 - 2026.

1. GRANT DESCRIPTION

Type of grant: Research Initiation Grant (BII)

General scientific area: ENGINEERING,COMPUTER SCIENCE

Scientific subarea: Computer engineering,Programming

Workplace: INESC TEC, Porto, Portugal

Maintenance stipend: 651.12, according to the table of monthly maintenance stipend for FCT grants (https://www.fct.pt/wp-content/uploads/2024/02/Tabela-de-Valores-SMM_atu… ), 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 Annex II), up to a maximum limit of 50 of the monthly maintenance stipend.

The grant holder will benefit from health insurance, supported by INESC TEC.

2. OBJECTIVES:

Investigate the applicability of Large Language Models (LLMs) for forecasting tasks in the energy sector ;
Benchmark LLM performance against traditional forecasting methods ;
Develop small prototype to conduct simulations over real datasets;

3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING:

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.

4. REQUIRED PROFILE:

Admission requirements:
Electrical Engineering, Informatics, Computer Science or similar courses
The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions.

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;

Minimum requirements:
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)

5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS:

Selection criteria and corresponding valuation: the first phase comprises the Academic Evaluation (AC), based on the criteria referred to in Article 12 of the Regulations for Grants of INESC TEC, while the second phase comprehends the Individual Interview (EI).
All factors are evaluated on a scale of 0 to 100, taking into account the applicants' merit, suitability and conformity with the preference factors.
The weight of the AC factors are as follows: Academic Qualifications (FA, 50), Scientific Publications (PC, 20), Experience (EX, 20) and Motivation Letter (CM, 10).
Candidates who score less than 50 points in the AC average will be considered excluded on absolute merit. The top five candidates approved on absolute merit will be qualified for the individual interview. The Final Grade (CF) is obtained by the weighted average of AC (70) and EI (30).

DISABILITY INCENTIVE

Candidates who present a degree of disability equal to or greater than 90% will benefit from an incentive (20) in the score of the CV Assessment.
Candidates who present a degree of disability equal to or greater than 60% and less than 90% will also benefit from an incentive (10) in the score of the CV Assessment.
Said score may, in these cases, exceed 100 points.
Candidates must demonstrate the degree of disability during the application, namely through the submission of the Multi-Purpose Medical Certificate of Disability, issued in accordance with Decree-Law no. 202/96, of October 23 - currently in effect.

The Selection Jury is composed of the following members:
President of the Jury: Ricardo Jorge Bessa
Full member: Ricardo Jorge Bessa
Full member: Carla Silva Gonçalves
Substitute member: Manuel Matos

Release of results and prior hearing: the results of the selection process, as well as the terms and procedures for prior hearing, will be released to the applicants by email, under the terms referred to in Article 13 of the Regulations for Studentships and Fellowships of INESC TEC.

6. FORMALISATION OF APPLICATIONS:

Application Documents:
1. Motivation letter;
2. Curriculum Vitae (must include the list of previous fellowships, their type, beginning and end dates, funding entities and host institutions);
3. Certificate or diploma degree;
4. Proof of enrollment in a degree awarding study cycle or in a non degree awarding Higher Education program.
- The proof of enrollment may be presented just during the grant hiring stage.
5. Signed declaration stating not having benefited from any other research fellowship (Article 5, no. 5)
6. Documental evidence to support the country of residence, residence permit or other legally equivalent document, in cases where the applicant is a foreigner or non-resident in Portugal - valid until the beginning of the grant.
7. Other supporting documents relevant to the final assessment.

Failure to deliver the required documents within the 90-day period after the date of the notice of the conditional awarding of the grant implies its cancellation.

Application period: From 2025-06-01 to 2025-06-30

Submission of applications: the application will be formalised by submitting the form available in the Work With Us section of INESC TEC website.

7. BINDING LEGISLATION AND REGULATION

The hiring process shall comply with the current legislation regarding the Research Grant Holder Statute, approved by Law no. 40/2004 of August 18, in its current wording, as well as by the Regulations for Grants of INESC TEC and for https://files.dre.pt/2s/2019/12/241000000/0009100105.pdfFCT Grants Regulation in force.

For more information, please check the Regulations for Grants of INESC TEC and relevant annexes at www.inesctec.pt/bolsas

Academic qualifications: Electrical Engineering, Informatics, Computer Science or similar courses.
Minimum profile: 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;
.

Research Field Engineering » Computer engineering Years of Research Experience None

Research Field Computer science » Programming Years of Research Experience None

Additional Information

Maintenance stipend: 651.12 euros, according to the table of monthly
maintenance stipend for FCT grants (https://www.fct.pt/wp-content/uploads/2024/02/Tabela-de-Valores-SMM_atu… ), 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 Annex II), up to a maximum limit of 50% of the monthly maintenance stipend.

Costs attributable to INESC TEC may include registration, enrolment or tuition fee stipend, either directly or through reimbursement, during the grant duration.

The grant holder will benefit from health insurance, supported by INESC TEC.

Selection process

5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS:

Selection criteria and corresponding valuation: the first phase comprises the Academic Evaluation (AC), based on the criteria referred to in Article 12 of the Regulations for Grants of INESC TEC, while the second phase comprehends the Individual Interview (EI).
All factors are evaluated on a scale of 0 to 100, taking into account the applicants' merit, suitability and conformity with the preference factors.
The weight of the AC factors are as follows: Academic Qualifications (FA, 50), Scientific Publications (PC, 20), Experience (EX, 20) and Motivation Letter (CM, 10).
Candidates who score less than 50 points in the AC average will be considered excluded on absolute merit. The top five candidates approved on absolute merit will be qualified for the individual interview. The Final Grade (CF) is obtained by the weighted average of AC (70) and EI (30).

DISABILITY INCENTIVE

Candidates who present a degree of disability equal to or greater than 90% will benefit from an incentive (20) in the score of the CV Assessment.
Candidates who present a degree of disability equal to or greater than 60% and less than 90% will also benefit from an incentive (10) in the score of the CV Assessment.
Said score may, in these cases, exceed 100 points.
Candidates must demonstrate the degree of disability during the application, namely through the submission of the Multi-Purpose Medical Certificate of Disability, issued in accordance with Decree-Law no. 202/96, of October 23 - currently in effect.

The Selection Jury is composed of the following members:
President of the Jury: Ricardo Jorge Bessa
Full member: Ricardo Jorge Bessa
Full member: Carla Silva Gonçalves
Substitute member: Manuel Matos

Release of results and prior hearing: the results of the selection process, as well as the terms and procedures for prior hearing, will be released to the applicants by email, under the terms referred to in Article 13 of the Regulations for Studentships and Fellowships of INESC TEC.