10 Sep 2026
Job Information
- Organisation/Company: INESC TEC
- Research Field: Engineering » Industrial engineering
- Researcher Profile: First Stage Researcher (R1)
- Application Deadline: 23 Sep 2026 - 23:59 (UTC)
- Country: Portugal
- Type of Contract: Temporary
- Job Status: Full-time
- Hours Per Week: 36
- Offer Starting Date: 15 Oct 2026
- Is the job funded through the EU Research Framework Programme?: Not funded by a EU programme
Offer Description
Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0267.pdf
CALL FOR APPLICATIONS: RESEARCHER
Job/position/grant:
Job reference: AE2026-0267 (AIOTacitR-SYSTEM)
INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência
Job/position/grant: RESEARCHER
City: Porto
Research field: Main: ENGINEERING
Sub: Industrial engineering
Job summary:
INESC TEC is accepting applications for 1 RESEARCHER job in the Last mile delivery problem
Project: Otimização Impulsionada por IA para Entrega de Última Milha Sustentável
Scientific Advisor: António Galrão Ramos
Start Date: from 2026-10-15 to
Location: INESC TEC, Porto, Portugal
Job description:
Work Area: Last mile delivery problem
Project overview: The work plan for the AI-Driven Optimization for Sustainable Last Mile Delivery (AIO-TacitR) project is structured around advancing solutions to the Vehicle Routing Problem (VRP) by leveraging Artificial Intelligence, Machine Learning, and Generative AI to improve sustainability and real-time adaptability in urban logistics. The project begins with foundational activities such as literature review, data collection, and preprocessing to build a robust knowledge base. It then advances to developing and refining AI models that integrate zone characteristics, traffic conditions, weather, and driver feedback. These models aim not only to reduce delivery times and operational costs but also to minimize environmental impacts, aligning with sustainability goals. The plan emphasizes iterative development, incorporating feedback loops from testing and environmental analysis to continuously improve performance.;
Over the timeline, the project follows a phased approach: Year 1 focuses on groundwork—research, data preparation, and initial AI model prototypes. Year 2 emphasizes refinement, prototype testing, and detailed environmental impact assessments, while Year 3 prioritizes integration of AI systems into partner logistics operations, user experience design, validation, and large-scale dissemination of results. Cross-cutting activities like project management, stakeholder engagement, and sustainability reporting ensure the alignment of research outputs with industry needs and societal goals. The structured plan ultimately seeks to bridge the gap between theoretical optimization models and real-world delivery challenges, setting a foundation for greener, more efficient, and socially responsible logistics solutions.
Objectives: The main objective of the work is to develop a sustainable artificial intelligence based system to solve last mile delivery problem, while considering different factors.
Academic Qualifications: Master Student in Industrial Engineering, Informatics Engineering, Mechanical Engineering, Computer Science, Civil Engineering or related area
Minimum profile required: - Experience in the following skill(s):
- Programming in Python
- Ability to work in group, high autonomy, and organization
-Experience in working with datasets
- Basic knowledge of ETL process
Preference factors: - Basic Knowledge of data science and engineering;
- Experience in developing machine learning models and data analysis;
- Experience in working with data;
Funding Entity: within the scope of the project AIOTacitR with reference 15051 (COMPETE2030-FEDER-00870300) co-funded by the ERDF - European Regional Development Fund through Innovation and Digital Transition Program - COMPETE 2030 under the scope of Portugal 2030 and by National Funds through the FCT - Fundacao para a Ciencia e a Tecnologia, I.P. (Portuguese Foundation for Science and Technology).
Type of contract: Uncertain term contract
The hiring shall be governed by what is stipulated in the legislation in force regarding fixed individual employment contracts and by INESC TEC norms.
Selection criteria: The selection of the candidates will be based on the following criteria, in descending order of consideration:
a) Relevant Curriculum in the concerned field of this tender
b) Proven experience.
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.
Selection Jury: President of the Jury: António Galrão Ramos
Member: Farzam Salimi
Member: Pedro Filipe Rocha
Notification of results: The results of the selection process will be sent to the interested by electronic mail.
Application period: From 2026-09-10 to 2026-09-23.
Application submission: Electronic form filling in www.inesctec.pt in the section Work with Us.
Where to apply
Website: https://www.inesctec.pt/en/opportunity/AE2026-0267
Requirements
- Research Field: Engineering
- Education Level: Bachelor Degree or equivalent
Specific Requirements
Academic qualifications: Master Student in Industrial Engineering, Informatics Engineering, Mechanical Engineering, Computer Science, Civil Engineering or related area.
Minimum profile: - Experience in the following skill(s):, - Programming in Python, - Ability to work in group, high autonomy, and organization, -Experience in working with datasets, - Basic knowledge of ETL process.
Preference factors: - Basic Knowledge of data science and engineering;
- Experience in developing machine learning models and data analysis;
- Experience in working with data;.
- Research Field: Engineering » Industrial engineering
- Years of Research Experience: None
Additional Information
Selection process
Selection criteria: The selection of the candidates will be based on the following criteria, in descending order of consideration:
a) Relevant Curriculum in the concerned field of this tender
b) Proven experience.
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.
Selection Jury: President of the Jury: António Galrão Ramos
Member: Farzam Salimi
Member: Pedro Filipe Rocha
Notification of results: The results of the selection process will be sent to the interested by electronic mail.
Application period: From 2026-09-10 to 2026-09-23.
Application submission: Electronic form filling in www.inesctec.pt in the section Work with Us.
Website for additional job details: https://www.inesctec.pt/en/opportunity/AE2026-0267
Work Location(s)
- Number of offers available: 1
- Company/Institute: INESC TEC
- Country: Portugal
- City: Porto
- Postal Code: 4200-465
- Street: Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias
Contact
- City: Porto
- Website: http://www.inesctec.pt
- Street: Campus da FEUP - Rua Dr. Roberto Frias
- Postal Code: 4200-465 Porto
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