Contacts 
Printable version"A Multimodal Deep learning framework for neurodevelopmental trajectories”, financed by the Fondazione Bruno Kessler- FBK
Description:
This project will explore the design of deep neural networks architectures to elucidate the dynamics of human cognitive development, both in physiological and pathological situations, with a focus on developing new quantitative methods in longitudinal studies. In particular we expect to provide new tools for observation in standardized or naturalistic set-ups, e.g. integration of video material, phenotype data, psychological tests. The ideal candidate will be strongly motivated in developing skills in data science and machine learning to implement predictive models able to forecast the evolution of developmental trajectories along time, possibly also integrating biomedical aspects. The PhD position is offered with the TRAIN joint PhD program of UniTN/FBK, which aims at an interdisciplinary research in the domain of Autistic Spectrum Disorders. Joint positions will be offered at DIPSCO and Fondazione Kessler, Data Science Area, Predictive Models for Biomedicine & Environment Lab (FBK/MPBA http://mpbalab.fbk.eu/).
Skills: familiarity with quantitative data analysis, proven basic knowledge of deep learning (e.g. TensorFlow or PyTorch), experience in analytics of developmental data and basic working knowledge of data science tools (e.g. Python, R). .
Responsible of the Project:
Cesare Furlanello: furlan [at] fbk.eu
"Digital Technologies for mindfulness meditation", financed by the Fondazione Bruno Kessler- FBK
Description:
the project intends to investigate how digital technologies can support and improve the practice of mindfulness meditation. A first goal of the project is experimenting novel techniques, both physiologically accurate and nonintrusive, to monitor the level of attention/distraction during a mindfulness meditation exercise (e.g. the measurement of pupils’ size by eye tracking). These techniques will be assessed by comparison with more complex neuroimaging methods (EEG or fMRI). A second, related goal, is experimenting with different stimuli (visual, acoustical, haptics or other modalities) to effectively support the practice of active focalization for expert and novice meditators.
Skills (partially required and partially to be acquired during the research work period): data analysis, evaluation of technologies (experimental design, quantitative analysis, etc.) as well as programming skills to develop prototypes. .
Responsibles of the Project:
Massimo Zancanaro: zancana [at] fbk.eu
Nicola De Pisapia: nicola.depisapia [at] unitn.it
"Genes, environmental factors and neuroimaging: A data-driven approach to economic behavior", financed by the Fondazione Bruno Kessler– FBK
Description:
Economic choices are a crucial part of our daily routines, and they are part of a process that involves personal traits (personality as well as genetic component), education and cultural background. Starting from insight from the physiological tradition, as well as from the late genetic research on decision-making mechanisms, this study aims to investigate how polygenic factors interact with the environment to modulate people decision making (especially in the domain of economic decisions). We hypothesize that economic choices are related to (1) genetic components, (2) environmental factors and are linked to (3) explicit attitudes that are traceable on social media usage. Furthermore, we hypothesize the existence of (4) specific economic behavioural individual profiles that can be predicted by the multilevel assessment of genetic, environment and social media engagement. In order to investigate these relationships, we aim to conduct a number of experimental studies on economic decision making and neuroimaging data acquisition (using fNIRS or fMRI). Furthermore, using big data from economic validated sources (i.e. credit card usage), we aim to create association patterns between social media activities and economic decisions to profile cluster of individuals.
Skills: familiarity with quantitative data analysis, proven basic knowledge of machine learning, experience in analytics of genetic and fMRI data and good working knowledge of data science tools (e.g. Python, R). .
Responsible of the Project:
Bruno Lepri: lepri [at] fbk.eu
Gianluca Esposito: gianluca.esposito [at] unitn.it
"Gamification for linguistic annotations", financed by the Fondazione Bruno Kessler– FBK
Description:
The project intends to investigate the use of gamification techniques in the collection of linguistic annotations. While “games with a purpose” have been proposed so far to make tedious manual annotation funny or appealing, but have been limited to straightforward annotation tasks such as the linking of anaphoric antecedents or the identification of synonyms, we aim with this Phd thesis both to improve the range of gamification techniques to be tested, and to extend such techniques to more challenging tasks like the creation of textual entailment datasets or the annotation of corpora for summarization tasks.
Skills (partially required and partially to be acquired during the research work period): data analysis, evaluation of technologies (experimental design, quantitative analysis, etc.) as well as programming skills to develop prototypes
Responsible of the Project:
Sara Tonelli: satonelli [at] fbk.eu
"Learning and digital technologies”, research fellowship for doctoral studies financed by the Edizioni Centro Studi Erickson – Trento
Note:
The PhD student awarded the scholarship financed by FBK is obliged to maintain confidentiality in regard to the disclosure and use of any information, data, software, discovery, invention, idea, method, process (in any format including the source code) or other knowledge discovered, conceived, developed and/or implemented within the research activities financed relatively not only to the object of the PhD scholarship awarded to the PhD student, but also to possible changes made to the object of the grant as agreed with FBK.