Check out Professor Adriano Carvalho’s (SYSTEC/ARISE) Interview

Check out the interview with Professor Adriano Carvalho (LEMR head) in the FEUP engineering official magazine.

Teaching Factories Competition 2023: DEEP TECH – Universities & VET

EIT Manufacturing is actively seeking talented Solver Teams (students) from Universities and VETs (vocational education and training) to address seven selected challenges related to DeepTech from industrial companies. Teaching Factories serve as dynamic co-creation spaces where real-world manufacturing challenges intersect with the innovative thinking of students, professors, and industry practitioners.
This platform encourages universities/VETs to submit their applications to develop cutting-edge solutions around Deep Tech. The competition provides a unique platform for students to showcase their skills and innovations in manufacturing.
What you will receive:

  • Long-term cooperation opportunities with industrial companies
  • Intensive innovation and entrepreneurship coaching and learning courses
  • International working experience
  • Financial prizes

Join an informative webinar on December 19th at 13:00 CET. Registration is available here.
Do you have a question? Feel free to get in touch with our team via email at tfcompetition@eitmanufacturing.eu . For more information, please visit

Master’s thesis under ARISE is awarded 1st place in the REN 2023 Prize

Nuno Alexandre Gonçalves Mendes, currently PhD student at the University of Coimbra, supervised by Pedro Moura, from the Institute of Systems and Robotics (ISR-UC), and Jérôme Mendes, from CEMMPRE (both members of the Associated Laboratory ARISE), won 1st place in the REN 2023 Prize. Nuno Mendes’ dissertation, titled “Federated Learning for the Prediction of Net Energy Demand in Communities of Buildings”. REN Prize, created in 1995, is awarded yearly to the best master’s thesis in energy developed at Portuguese universities. The award ceremony took place in Lisbon at the Salão Nobre of the Ritz Hotel on November 13.

Future energy communities allow for the local optimization of resources, namely through energy trading between buildings, but for this, it is crucial to have forecasts of energy consumption and generation in buildings. Conventionally, these forecasts use historical data on net energy consumption in buildings, but they can be improved by also including private building information. Building automation systems allows for the collection of large amounts of data that forecasting systems can use, but this data is mostly classified as private. In this context, Federated Learning (FL) has been used in several areas with the main objective of protecting users’ private data.

This dissertation proposes a new approach for predicting net energy in energy communities based on a FL system. The structure implemented includes the integration of third-party entities as data providers and two forecasting systems (one for consumption and one for generation), both managed by the same server, which ensure the forecasting of net electricity consumption, independently, in each of the buildings belonging to the energy community. The results obtained show that the forecasting systems have achieved a high level of accuracy. Above all, they enable a high level of adaptability, for example, to seasonal variations, the entry and exit of buildings from the community or even new communities made up of other buildings.

Sim2Adapt: Open Call

Positions available on Sim2Adapt: Multiscale approaches to improve the application of self-adaptive coatings in low friction mechanical systems (PhD Students)

University of Coimbra opened two PhD student positions (duration of the contract planned for 3 years).

The positions will focus on combining numerical and experimental multi-scale approaches to model the contact conditions, to improve knowledge concerning self-adaptive coatings and, consequently, support their controlled application for a wide range of sliding contact conditions. The fellowships will be involved in the execution of the following tasks:

  1. Feature extraction and metamodeling of TMD-base systems;
  2. Wear track mapping, including the study of the selected coating against softer and
  3. Development of contact mechanics multiscale models for self-adaptive coatings.

The research work will be carried out at CEMMPRE and LED&Mat (IPN).

Necessary qualifications: Master’s degree in Mechanical Engineering, Informatics Engineering, Data Science and Engineering, Industrial and Management Engineering, Civil Engineering or Materials Engineering, and enrolled in a Ph.D. course or enrolled in a course not conferring an academic degree, with interest in numerical modelling.

Deadline: 30 October 2023

More information is available here.

SYSTEC & ARISE Researchers Proposal Approved for FCT Funding

Proposal by the SYSTEC researchers entitled “Speech recognition for the sound reconstructed using a bio-inspired geometric model” has been approved by the Fundação para a Ciência e a Tecnologia in the framework of the Call on Advanced Computing Projects: Artificial Intelligence in Cloud (2nd edition), more info here. Credits on the Google Cloud Platform equivalent to USD 74.100 have been granted. The call was intended to support research projects in Natural Language Processing, Ethics in artificial intelligence and other scientific areas where the Google Cloud Platform facilities in artificial intelligence and data analysis algorithms are useful. The granted computational resources will be used at the SYSTEC to solve computationally demanding problems involving machine learning methods in the framework of an international collaboration with French Universities. The computational project will be executed under coordination by the Principal Investigator, Dr. Roman Chertovskih and Co-Principal Investigator, Dr. Rui Jorge Pereira Gonçalves.

CEMMPRE Researcher receives Scientific Merit Medal at “Ciência 2023”

Teresa Vieira, Researcher at CEMMPRE – Centre for Mechanical Engineering, Materials and Processes and Associated Laboratory ARISE – Advanced Production and Intelligent Systems, was awarded the Medal of Scientific Merit by the Ministry of Science, Technology and Higher Education, during the initiative “Ciência 2023 – Portuguese Science Summit”, on 5 July 2023.

This distinction is intended to reward national or foreign individuals who, due to their high professional qualities and fulfilment of duty, have distinguished themselves for their valuable and exceptional contribution to the development of science or scientific culture in Portugal.

SYSTEC’s Dr Roman Chertovskikh receives approval for Advanced Computing project

Dr Roman Chertovskikh, an integrated member of ARISE-LA/SYSTEC, has achieved a significant milestone in his research endeavours. His proposal was recently approved by the Fundação para a Ciência e a Tecnologia (FCT) in the 3rd call for Advanced Computing Projects. This highly competitive call, organized by the National Network for Advanced Computing (RNCA) and facilitated by the FCCN (FCT’s National Scientific Computing Unit), seeks to promote advancements in the field of advanced computing.

Dr Chertovskikh’s successful proposal has garnered him three million core hours on the state-of-the-art Oblivion supercomputers,  hosted at the High-Performance Computing Center of Évora University. This allocation, estimated to be worth around 40 thousand euros, will greatly enhance SYSTEC’s computational capabilities. It serves as a valuable resource for ongoing and future simulations involving large distributed systems, particularly in the areas of fluid dynamics and magnetohydrodynamics.

The recognition bestowed upon Dr Chertovskikh’s proposal is a testament to ARISE-LA/SYSTEC’s excellence in research and innovation. Notably, SYSTEC’s proposals have consistently received support from the FCT in all calls for Advanced Computing Projects. This year, Dr Chertovskikh’s project stood out by achieving the highest classification in the evaluation panel dedicated to Earth and Environmental Sciences. Furthermore, it is the only proposal within that panel to secure 100% of the requested resources.

ARISE-LA/SYSTEC expresses its satisfaction and gratitude for the FCT’s unwavering support. Such recognition and allocation of resources enable the team to push the boundaries of scientific discovery and technological advancements. The computational power provided by the Oblivion supercomputers will further propel SYSTEC’s research capabilities, allowing for intricate and comprehensive simulations in their pursuit of knowledge.

  • For more details regarding the 3rd call for Advanced Computing Projects, please visit [1]. 
  • To learn more about the National Network for Advanced Computing (RNCA), visit [2],
  • For information on the FCCN (FCT’s National Scientific Computing Unit), visit [3]. 
  • Additional information about the Oblivion supercomputers can be found at [4].

Open Call for PhD student positions (CEMMPRE-UC)

Positions available on Sim2Adapt: Multiscale approaches to improve the application of self-adaptive coatings in low friction mechanical systems (PhD Students)

University of Coimbra opened two PhD student positions (duration of the contract planned for 3 years).

The positions will focus on combining numerical and experimental multi-scale approaches to model the contact conditions, to improve knowledge concerning self-adaptive coatings and, consequently, support their controlled application for a wide range of sliding contact conditions. The fellowships will be involved in the execution of the following tasks:

  1. Feature extraction and metamodeling of TMD-base systems;
  2. Wear track mapping, including the study of the selected coating against softer and
  3. Development of contact mechanics multiscale models for self-adaptive coatings.

The research work will be carried out at CEMMPRE and LED&Mat (IPN).

Necessary qualifications: Master’s degree in Mechanical Engineering, Informatics Engineering, Data Science and Engineering, Industrial and Management Engineering, Civil Engineering or Materials Engineering, and enrolled in a Ph.D. course or enrolled in a course not conferring an academic degree, with interest in numerical modelling.

Deadline: 17 May 2023