Printable versionPNRR grants
Thanks to DM 629/2024 and DM 630/2024, this year the University of Trento was granted 40 PNRR PhD grants. These grants will go through the process of a separate call and selection. The PNRR call will open in June/July. The selection (title evaluation and interview) process will likely take place in the second half of July and early September.
The following were the topic-specific grants for the 3 PNRR positions in 2023's Doctorate in Cognitive and Brain Sciences:
1. Neural bases and computational mechanisms of abstraction in the human brain (M. Piazza and R. Bottini, n.1 grant)
Humans have extraordinary abstraction skills: starting from the vast complexity of the stimuli of the world, our mind spontaneously organizes the data in simple and essential structures, which allow us to recognize the commonalities between situations which are superficially different and, in doing so, resolute novel challenges never encountered before.
The doctoral project aims at deepening our understanding of the neurocognitive bases of his ability by combining cognitive neuroscience and computational modelling approaches.
In particular, the objectives of this research are:
- Investigate the neurofunctional mechanisms of abstraction intended as learning of structures and their involvement in generalization ed inferences in healthy adult humans (methods: fMRI, MEG, and eye tracking).
- Contribute to the formalization of the learning of structures in computational terms and explore the possibility of integrating the learning of structures in Artificial Intelligence (AI) architectures
- Assess the learning impairment of structures due to pathologies such as disorders of the development and/or ageing.
Supervisors: Manuela Piazza and Roberto Bottini
2. Dynamic modeling of predictive representations in the brain using magnetoencephalography (MEG)-based dynamic representational similarity analysis (M. Wurm, n.1 grant)
To navigate the dynamic world, our brain needs to continuously update its representation of external information and generate predictions of future states. Without such predictions, there would be a substantial time lag between states in the real world and the perception of, and reaction to, these states. A fundamental assumption is that the brain constantly generates and updates internal models of the world. However, the representational nature of internal models at different processing levels, and how the dynamics of internal models temporally relate to (e.g. follow or predict) actual events in the real world, remains unknown.
The objective of this PhD project is to investigate the representational dynamics in the brain in response to dynamic events using a novel, magnetoencephalography (MEG)-based approach – dynamic representational similarity analysis (dRSA hereafter). The innovation of the approach is to use temporally variable models of representational similarity to characterize representational content at each time point during temporally extended, unfolding events. This allows testing whether a given time point is represented in a lagged bottom-up manner or in a predictive top-down manner, that is, before it actually occurred (for reference to the method, see https://doi.org/10.1101/2022.09.02.506366).
The successful candidate should have a background in cognitive neuroscience and neuroimaging (preferably M/EEG or fMRI, MVPA/RSA) and strong programming skills (preferably Matlab). For further information, candidates are strongly encouraged to get in contact with the PI.
Supervisor: Moritz Wurm
3. Neurocognitive fitness and aging (V. Mazza, n.1 grant)
The project will investigate physiological aging from a cognitive and neural point of view, using behavioral, neuroimaging (e.g., EEG and fMRI) and neurostimulation tools. There are two objectives.
First, to identify cognitive and neural markers of aging in various cognitive domains, such as perception, attention and working memory. The project will look into basic mechanisms (e.g, loss of cortical specialization) that may explain the effects of cognitive aging. Second, to identify the best practices to potentiate learning in older individuals during attentive and memory tasks by means of cognitive training and neurostimulation, based on the results of the first phase. In both phases, the project will focus on individual differences and on their role in determining the trajectories of healthy aging.
The project is in collaboration with Prof. Andrew Bagshaw, Centre for Human Brain Health, University of Birmingham (UK); the PhD student will be spending a 12-month research period in Prof. Bagshaw's lab with a particular focus on developing knowledge of neuroimaging methods and their applications.
Ideal candidates would have a background or strong interest in cognitive neuroscience; prior experience in data collection, preferably in the older population; prior experience in the use of methods relevant to the project (e.g., EEG, fMRI, neurostimulation); prior coding experience, preferably in python or Matlab; fluency in Italian and English.
Supervisor: Veronica Mazza