The CSIC offers scholarships for the introduction to a research career in the context of the JAE Intro Programme. The IIIA offers seven projects to be carried out at our research institute:
Projects and mentors:
Machine Learning for Prediction of Epilepsy Crisis from Electroencephalograms (JAEINT24_EX_0607)
Mentor: Jesús Cerquides
This research project aims to employ machine learning methodologies to predict epilepsy crises by analyzing publicly available electroencephalogram (EEG) data. Recognizing the challenges associated with timely prediction of epilepsy seizures, the study focuses on utilizing existing datasets to develop a predictive model. By leveraging machine learning algorithms, particularly deep learning models, the research will involve preprocessing publicly accessible EEG data, extracting relevant features, and implementing advanced classification techniques. The objective is to create a reliable and accurate predictive tool capable of discerning patterns and subtle changes in EEG signals preceding epileptic seizures.
New Variants of the MiCRO Negotiation Strategy (JAEINT24_EX_0826)
Mentor: Dave de Jonge
BACKGROUND: The topic of automated negotiation deals with the question how autonomous software agents can negotiate with each other. Specifically, it deals with scenarios in which two or more agents need to solve a problem together, even though they have conflicting interests. This means that the agents need to compromise and find a solution that is acceptable to everyone. In order to come to an agreement, the agents may propose solutions to each another, and each agent may accept or reject the proposals it receives from the other agents. A typical example is the case of a buyer and a seller that are bargaining over the price of a car. While the seller aims to sell the car for the highest possible price, he still needs to make sure the price is low enough for the buyer to accept the deal. Recently, an extremely simple new negotiation algorithm, called MiCRO, was introduced by Dr. Dave de Jonge which was shown to outperform almost all existing state-of-the-art negotiation algorithms, even though MiCRO is much simpler than those other algorithms. Unfortunately, however, MiCRO is only applicable to negotiations between no more than two agents, and only to problems for which the number of possible solutions is relatively small (less than a million). To deal with these limitations, dr. de Jonge has proposed some ideas on how MiCRO could be generalized to negotiations among more than two agents, and to negotiations with a larger number of possible solutions (several millions). GOALS OF THIS PROJECT: The goal of this project is for the student to implement these ideas (in Java or Python), perform experiments, and determine how well these new variants of MiCRO perform against state-of-the-art negotiation algorithms, and under which parameter settings. And perhaps, based on the results of those experiments, the student could even figure out ways to improve MiCRO even further. Optionally, the task can be made more challenging, by trying to implement an even more advanced algorithm that is applicable to astronomically large test cases (e.g. with 10 to the power 100 possible solutions). This would require the use of more complex search techniques, such as genetic algorithms or tree search.
Advanced AI for Immersive Training Simulations (JAEINT24_EX_1586)
Mentor: Jordi Sabater-Mir
The project focuses on the development and implementation of realistic Non-Player Characters (NPCs) within simulated environments for training purposes. The objective is to enhance the immersive quality of training simulations by populating them with NPCs that exhibit lifelike behaviours and responses. The work involves designing and programming NPCs with advanced artificial intelligence algorithms including Large Language Models (LLMs) and other AI technologies, enabling them to adapt dynamically to changing scenarios, interact convincingly with trainees, and simulate a wide range of human-like behaviours. The goal is to push the boundaries of immersive training simulations, providing trainees with more realistic and challenging scenarios that better prepare them for real-world situations.
Neurosymbolic AI: from Theory to Applications (JAEINT24_EX_1596)
Mentor: Vicent Costa
Neurosymbolic artificial intelligence is a recent domain in artificial intelligence (AI) that seeks to merge the knowledge-based symbolic approach with neural network-based methods. It is mainly motivated by application-level regards (e.g., explainability and interpretability) and algorithmic-level considerations (e.g., long-term planning and analogy) and intends to merge the strengths of both approaches and overcome their corresponding drawbacks. The main goal of this project is to integrate principles and aspects from both approaches and to design hybrid systems in this emerging field of AI. The application domains would be related to tutors' previous works, especially to issues concerning people with different kinds of disability (e.g., evaluation of the quality of life of people with mental distress). The ideal candidates for this fellowship have excellent programming skills and knowledge of logic and theoretical computer science and are concerned with the ethical aspects of AI systems design.
For the requirements, please check Article 5 of the official call.
Applications need to be completed through the CSIC application portal (https://sede.csic.gob.es/intro2024). Please check Article 9 of the official call, for the details.
For more information, please contact Marco Schorlemmer <marco@iiia.csic.es>.