MISTI Spain

Galenicum

Galenicum is a dynamic pharmaceutical company created 20 years ago by three friends in their 20s (now 40s). The three friends remain still friends, and are very much involved in the business as co-CEOs of the companies within the Galenicum Group (B2B and B2C businesses). The company keeps a remarkable entrepreneurial spirit and energy, with young staff from both scientific backgrounds in Life Sciences, as well as business profiles and Engineers.  

The values of the company are quite embedded in the culture, in fact the culture was the origin of the values exercise, and they are:

Challenge - entrepreneurial, super dynamic
Connection - good relationships and interconnections within people, providers, clients - WFH 1 time a week only, lots of people in the office
Commitment - engaged with the company and focussed on results

Booster values are: Energy, Adaptability, Creativity

Integrity - critical at all levels

Galenicum has been working on ESG over the past years, both motivated by genuine belief and by the business and compliance needs. We have articulated a Strategy over the past 3 years that we are happy with, but we want to go beyond and understand what shall be next for Galenicum in ESG, understanding well the right benchmarks and remarkable initiatives in the field and recommending what shall be done in the next 3-5 years.

HM Hospitales

Comprehensive Cancer Center CLARA CAMPAL, Hyper-Early Cancer Detection Unit

Madrid, Spain

At a prestigious cancer center in Spain, university students rotate through a specialized Hyper-Early Cancer Detection Unit, where they gain valuable experience in both cutting-edge diagnostics and research through clinical trials. This unit is dedicated to detecting cancer at its earliest, often pre-symptomatic stages, using innovative techniques like liquid biopsies, advanced imaging, and genetic profiling. Students work alongside a multidisciplinary team of oncologists, radiologists, and researchers, observing how these advanced methods significantly improve early diagnosis and patient outcomes.

During the rotation, students are involved in patient assessments, learning to apply hyper-precise diagnostic tools that detect cancer in its most nascent stages. They also participate in clinical trials, where novel detection methods and treatments are tested, contributing to the research process that drives oncology forward.

This hands-on experience provides a deep understanding of both clinical practice and the critical role research plays in advancing early cancer detection. By combining patient care with innovative research, the program prepares future healthcare professionals to play a vital role in the evolving fight against cancer.

Universidad Nacional de Educación a Distancia (UNED)

Departamento de Física Interdisciplinar

The Laboratory of Infrared and Raman Spectroscopy (https://portalcientifico.uned.es/grupos/17451/detalle) conducts experimental and theoretical studies in polymer materials, and atmospheric and astrophysics systems. Our labs and our offices are located in Las Rozas de Madrid, which is a 30-40 minute bus ride from the Moncloa metro stop in Madrid, which is really close to the city.

Our lab is composed of 5 members with backgrounds in Physics, Chemistry, and Materials Science. We go to work to our university labs and offices every day of the week, although our schedules are flexible. All of us are young people that are active in sports (cycling, running, padel) and like to spend time outdoors. 

Our main project explores the use of photocatalysis to the degradation of micro and nanoplastics. Plastic is, nowadays, a ubiquitous material in our daily life, with global production close to 370 million tons. As everybody know, its environmental impact is a significant concern, as only a small fraction is recycled. The plastics accumulated in the environment can be fragmented, resulting in the so-called microplastics (MPs, <5 mm) and nanoplastics (NPlast, <1 μm). Effective elimination of plastic waste from the environment is a pressing research goal, but plastics are difficult to degrade due to their low water solubility, complex structure and non-biodegradability. Current methods are not enough to solve this question, making the exploration of alternatives essential. Photocatalytic technology has emerged as a promising solution to degrade polymeric materials, with a focus on MPs and NPlast. In this topic, we have available student projects in areas 5, 8 and 10 to explore the role of photocatalysis for the plastic degradation. We will generate highly porous nanostructures of different catalysts to degrade commercial plastics analogues produced in our labs. Our basic studies, both experimental and theoretical, allow us to extract fundamental conclusions of these processes, and to acquire valuable insights for further advancements in this field.

The student will conduct experiments (under supervisor) to produce nanoporous photocatalysts and will tests their activity on the degradation of thin films of usual polymers.

Universidad Politécnica de Madrid

Department of Electrical Engineering, Electronics, Automation and Applied Physics 

Our team at ETSIDI, focused on the research group topic Low and high voltage networks and installations, fosters a culture of innovation, collaboration, and excellence. We are a diverse group of professionals and students who are passionate about electrical engineering. Our office environment is dynamic and supportive, encouraging creativity and continuous learning. We value teamwork and believe that the best solutions come from sharing ideas and working together. With a strong emphasis on real-world applications and industry partnerships, our culture is geared towards preparing students for successful careers and advancing the field of electrical engineering. We have extensive experience in participating in research projects and in international collaboration.

Over the past decade, retrofilling of mineral oil-filled transformers with natural or synthetic esters has expanded significantly. The main reasons for retrofilling a transformer are to reduce the fire and environmental risk of the equipment, as well as to increase its load carrying capacity and service life. Natural and synthetic esters are biodegradable materials and have much higher flash points than mineral oil. Furthermore, the use of esters has been shown to reduce the aging rate of the transformer’s solid insulation. Since the long-term overload capability of a transformer is limited primarily by the aging rate of its solid insulation, retrofilled transformers can operate at higher loads without decreasing their service life. In this project, the impact of retrofilling on the transformer’s dielectric behavior and the impact of retrofilling on the service life of the solid insulation are investigated. In addition, aspects related to the maintenance and operation of retrofilled transformers are investigated.

This project is funded by the Spanish Government and it is a coordinated project in which several institutions and companies are involved on.

The next task will be developed by the group led by Ricardo Albarracín Sánchez.

Modelling of electric field distribution in retrofilled transformers
The electric field distribution of the transformer representative geometries selected in this task will be analysed using Finite Element Method (FEM) and considering different voltages waveforms (i.e., AC, lightning impulse full-wave and impulse chopped-wave). The dielectric margins of the different parts of the insulation will be calculated and compared with the dielectric margins of the transformer insulation before retrofilling.

Developing a guide on the need for transformer derating or design review.
The conclusions extracted from previous tasks will be compiled to produce guidance on whether a transformer design must be re-evaluated to assure the safety of its retrofilling with an alternative insulating fluid. The guide will be presented to actors involved in transformer retrofilling.

Cíclica Arquitectura SCCL

In Cíclica, one of the services we offer is the planning of renovations of the built environment through Urban Building Energy Model (UBEM) methodology, to promote massive intervention in the residential stock and reach the EU’s decarbonisation commitments.

The methodology allows the integration of information from different databases to generate a repository of information on the national residential stock open to all agents and connected to the EU Observatory.

The knowledge generated, as relevant indicators and guidelines for renovation, allows defining the general vision and the long-term roadmap to guarantee a decarbonized and highly efficient residential stock, incorporating social, economic and environmental criteria. Ultimately, it guides and facilitates decision-making by the competent administrations.

Since a large part of the projects developed in Cíclica are based on the analysis of the built environment through the methodology, as well as the realization of rehabilitation intervention proposals, we will most likely need support, creativity and talent in this area. 

Types of projects developed:
- Energy renovation strategies and plans at different scales: state, regional, metropolitan, municipal and neighborhood.
- Database of the study area with detail at the property level.
- Web platform for interaction and visualization of relevant indicators.
- Output files customizable and interoperable with existing tools.
- Studies defining Conservation and Renovation Areas.
- Energy renovation preliminary projects at a scale higher to the building.
- Collaboration and consultancy in projects and tenders.

The work to be carried out may vary depending on the needs and the project of the moment, from the improvement of rigorousness in obtaining the available data, to the analysis of results and general proposals for urban areas, as well as the revision and contribution of new ideas in the methodology of energy simulation. 

In short, the intern’s main goal would be to contribute to the research work to improve specific aspects of the methodology and to provide new architectural, social and environmental proposals.

Knowledge of architecture, urban planning and rehabilitation of the built environment. Solvency with AutoCAD (or other CAD programs), Excel and QGIS (or ArcGIS). Programming skills, such as Python or other languages, are a plus.

The Big Data Department at Fundación Progreso y Salud (FPS), under the Andalusian Regional Ministry of Health and Consumer Affairs, facilitates the secondary use of Andalusian healthcare data and the analysis of external healthcare datasets (MIMIC, eICU, HiRID) for research and innovation (R&I) in clinical practice and health management. Leveraging a robust human and technological infrastructure for massive data processing and AI, the department supports collaborative efforts to drive healthcare advancements. We specialize in developing and validating AI-driven clinical decision support systems, working with multidisciplinary teams to address unmet medical needs within the Andalusian Public Health System. 

1. Support for Research and Innovation (R&I). The department provides a service dedicated to generating knowledge through Artificial Intelligence and Big Data applied to R&I projects. This includes supporting the generation of knowledge by addressing a diverse range of clinical questions (peer-reviewed publications) and establishing databases that enable researchers to create new insights (e.g., the Open Data for Andalusian Intensive Care initiative). 

A few projects within this research line are: 

• ODACI: ODACI, a Spanish acronym that stands for Andalusian Intensive Care Open Data, is a collaborative open data healthcare initiative from Andalusian ICUs that, if deployed, could improve diagnosis and optimize the treatment of patients through the development of AI algorithms. The source data for ODACI is Diraya, an Andalusian interoperable electronic medical record storing information from almost 30 hospitals and more than 1,000 primary care centers, integrating data from an accumulated cohort of over 13 million people.
• MAP OR: Predictive system that indicates whether patients undergoing cardiac surgery are at risk of death, embolism, or kidney failure based on their individual data.
• Smart_breathing: Prediction of the development of VAP (ventilator-associated pneumonia) in patients undergoing invasive mechanical ventilation for more than 5 days, using tabular data and chest X-rays from MIMIC-IV. 

2. PRAETORIA (Andalusian Platform for the Development of Clinical Decision Support Systems). This service focuses on developing Clinical Decision Support Systems (CDSS) by training and validating decision-support tools through multidisciplinary teams composed of developers, data engineers, clinicians, and managers. These teams address specific use cases within the Andalusian Public Health System, facilitating the process of informed decision-making by healthcare professionals in the diagnosis, treatment and follow-up of patients. 

A few projects within this research line are:

• PEANUT (Clinical decision support system for predicting antimicrobial resistance in intensive care units). The aim of the project is to develop predictive models of antimicrobial resistance based on clinical and microbiological data to improve treatment and reduce unnecessary antibiotic use.
• SAIDIS (Intelligent Alert System for the Detection of Severe Suicide Attempts). Novel approach for suicide attempt prediction based on the analysis of electronic health records (EHRs) using open-source large language models (LLMs) and related AI systems.
• ARAI (Artificial Intelligence in Patients with Rheumatoid Arthritis and Risk of DILD). Develop a clinical decision support system for predicting the development of Diffuse Interstitial Lung Disease (DILD) associated with Rheumatoid Arthritis (RA) in patients treated at the Virgen Macarena University Hospital over a 5-year period. o trIAje (Optimization of Telephone Triage Using AI-Based Predictive Models). This project applies advanced AI techniques, including supervised learning, deep learning, and natural language processing, to improve telephone triage at the Andalusian Emergency Coordination Center. Using structured (triage questions) and unstructured data (free-text), a model will be developed to better classify critical emergencies like cardiopulmonary arrest, respiratory distress, chest pain, and stroke. The project aims to refine protocols by identifying key predictive variables, improving sensitivity and specificity, reducing false alarms, and enhancing emergency decision-making and care. 

3. EVIAS (Evaluation and Validation of Artificial Intelligence in Healthcare Solutions). The main objective of EVIAS is to guarantee the efficacy and added value of AI algorithms, as well as the safety of deploying them in clinical practice. This, in turn, supports institutions in enhancing the quality of their AI development. To achieve this, EVIAS consists of two main phases: the validation of the algorithm using real-world data from the Andalusian Public Health System through data science methodologies, and the assessment of AI-based health technologies using Health Technology Assessment (HTA) methods and processes.

Within the ASSESS-DHT project, funded by Horizon Europe and supported by the European Commission, methods required to validate and assess AI-based health technologies are being refined. This collaborative effort involves academic experts, professionals from HTA agencies, independent research organizations, and companies to propose and pilot an assessment framework suitable for digital health technologies, including those leveraging AI algorithms. FPS-based researchers will collaborate with international leaders to address challenges related to validating and assessing AI-based health technologies and other types of digital health technologies. This includes developing real-world validation methods for AI-based algorithms using local data and establishing an HTA framework to evaluate digital health technologies, including AIbased ones. A comprehensive review of protocols endorsed by HTA and regulatory agencies has been conducted, with publication slated throughout 2024.

Universidade da Coruña (University of a Coruña) - UDC

Centro de Investigacións en Tecnoloxías da Información e as Comunicacións (Center for Research in Information and Communications Technologies) - CITIC

A Coruña, Spain

https://citic.udc.es/en/home-english-2/ 

Your internship at CITIC and RNASA-IMEDIR research group will focus on clear, tangible objectives that will allow you to contribute meaningfully to the project while developing valuable skills in AI and cybersecurity. The key objectives for this internship are:

1. Assist in Building Foundational AI Models for Network Traffic: Your main task will be to assist in the design and development of foundational AI models capable of analyzing network traffic data. You will:
○ Collaborate on pre-training Transformer-based models using large, unlabeled datasets of network traffic data.
○ Fine-tune these models for specific network security tasks, such as detecting anomalies, identifying malware, or analyzing traffic patterns.
○ Explore how the foundational models perform across different network environments (e.g., IoT, 5G networks) and contribute to research on enhancing their generalization capabilities.

2. Analyze and Evaluate Model Performance: One of your core responsibilities will be to assess the generalization performance of the models across different network environments and tasks. You will:
○ Experiment with diverse datasets to evaluate how well the AI models adapt to new, unseen network conditions.
○ Identify any limitations in the models and suggest improvements for better performance in specific tasks like traffic classification or intrusion detection.
○ Contribute to enhancing the adaptability of AI models, ensuring they can handle the dynamic nature of network security threats.

3. Contribute to Network Security Applications: Your work will directly apply to solving real-world cybersecurity problems. You will:
○ Use AI models to improve the detection of cyber threats, such as anomalies in network traffic that signal potential attacks.
○ Apply the models to malware classification, helping to detect and prevent harmful software in network environments.
○ Investigate how AI can predict and mitigate network congestion or traffic bottlenecks, contributing to the overall security and efficiency of telecommunication networks.

4. Collaborate with a Cross-Disciplinary Research Team: As part of an interdisciplinary team, you will be encouraged to actively participate in discussions, brainstorming sessions, and research meetings. You will:
○ Work alongside researchers from diverse fields (AI, cybersecurity, telecommunications) to broaden your knowledge and approach to problem-solving.
○ Present your findings, propose ideas, and engage in critical discussions about improving the AI models and their applications in network security.

5. Gain Hands-On Experience with AI Tools and Network Security Protocols: Throughout the internship, you will develop technical skills that are highly valuable in the fields of AI and cybersecurity. You will:
○ Work with tools like TensorFlow, PyTorch, and other frameworks used for building and training AI models.
○ Learn about network security protocols, how to handle network traffic data, and how to apply AI to solve specific network-related challenges.

6. Engage in CITIC and RNASA-IMEDIR Activities and Network with Other Researchers: During your internship, you will actively participate in CITIC’s research activities and collaborate with members of the RNASA-IMEDIR group. You will:
○ Engage in networking opportunities with other researchers, fostering connections that could lead to future collaborations.
○ Participate in cybersecurity dissemination events within CITIC, gaining exposure to the broader cybersecurity community.
○ Attend internal seminars, workshops, and team discussions, ensuring you are integrated into the center’s ongoing activities and research culture.