We are looking for candidates for a pre-doctoral contract associated with the research project “Advanced Statistics and Data Science 2: New data, new models, new challenges” Project PID2023-148158OB-I00 funded by MCIU /AEI /10.13039/501100011033 / FEDER, UE. The main objective of this project is to address the challenges posed by new data sets (increasingly large and complex) and new ways of analyzing them (more flexible but less transparent than traditional statistical techniques). We propose to pursue five lines of research: (1) New directions in the interpretability and explainability of predictive models. (2) Data from wearable devices: A functional approach to data analysis. (3) EEG data: Contributions from functional data analysis and interpretability. (4) Data from graphs: Bayesian prediction and modelling. (5) Non-linear dimensionality reduction methods for big data.

Neuroscience is experiencing unprecedented growth in dataset size both within individual brains and across populations. Large-scale, multimodal datasets are transforming our understanding of brain structure and function, creating opportunities to address previously unexplored questions. However, managing this increasing data volume requires new training and technology approaches. Modern data technologies are reshaping neuroscience by enabling researchers to tackle complex questions within a Ph.D. or postdoctoral timeframe. I will discuss cloud-based platforms such as brainlife.io, that provide scalable, reproducible, and accessible computational infrastructure. Modern data technology can democratize neuroscience, accelerate discovery and foster scientific transparency and collaboration. Concrete examples will illustrate how these technologies can be applied to mapping brain connectivity, studying human learning and development, and developing predictive models for traumatic brain injury (TBI). By integrating cloud computing and scalable data-sharing frameworks, neuroscience can become more impactful, inclusive, and data-driven..

Natural language is strongly context-dependent and can be perceived through different sensory modalities. For example, humans can easily comprehend the meaning of complex narratives presented through auditory speech, written text, or visual images. To understand how complex language-related information is represented in the human brain there is a necessity to map the different linguistic and non-linguistic information perceived under different modalities across the cerebral cortex. To map this information to the brain, I suggest following a naturalistic approach and observing the human brain performing tasks in its naturalistic setting, designing quantitative models that transform real-world stimuli into specific hypothesis-related features, and building predictive models that can relate these features to brain responses. In my talk, I will present models of brain responses collected using functional magnetic resonance imaging while human participants listened to or read natural narrative stories. Using natural text and vector representations derived from natural language processing tools I will present how we can study language processing in the human brain across modalities, in different levels of temporal granularity, and across different languages.