Post-doc/PhD positions are open @ IIT-CNR, Pisa, Italy, on the following topics #1: Analysis of large-scale Online Social Networks #2: Distributed AI at the edge of the Internet #3: Internet of People ** Hosting University for PhD positions: IIT-CNR is part of the PhD program in Data Science (https://datasciencephd.eu/) hosted by the Scuola Normale Superiore (https://www.sns.it/en) Selected applicants shall apply to the official call of the PhD https://www.sns.it/en/admissions/phd/how-to-apply-for-the-phd-courses PhD positions ------------- ** Position type: doctoral fellowship, 3 years ** Starting date: fall 2019 ** Location: IIT-CNR, Pisa, Italy - http://www.iit.cnr.it/ ** Supervisors: Marco Conti, Andrea Passarella https://scholar.google.com/citations?user=KniFTD0AAAAJ https://scholar.google.com/citations?user=sesKnygAAAAJ ** Annual scholarship: EUR 17000 ** Application deadline: continuous evaluation, up until 28 July 2019 For all positions, it will be possible (and advised) to organise one visiting student period abroad (typically, 6 months) during the PhD. Post-doc positions ------------------ ** Position: postdoctoral fellowship, 12 months (renewable) ** Starting date: ASAP from September 2019 (earlier starting dates can be considered as well) ** Location: IIT-CNR, Pisa, Italy - http://www.iit.cnr.it/ ** Supervisors: Marco Conti, Andrea Passarella https://scholar.google.com/citations?user=KniFTD0AAAAJ https://scholar.google.com/citations?user=sesKnygAAAAJ ** Entry-level salary: EUR 1625-1920 per month (net) depending on experience ** Application deadline: continuous evaluation, up until 28 July 2019 Position #1: Analysis of large-scale Online Social Networks ----------------------------------------------------------- Position type: Post-doc or PhD Job description --------------- The activities will be focused on BigData analytics applied to data crawled from Online Social Networks. Specifically, the subject of the PhD/Post-doc will be on (i) collecting large-scale datasets from popular OSNs (e.g., Twitter), and analyse the social network structures and the patterns of interactions between users through Big Data analytics techniques, with applications to the analysis of complex socio-technical phenomena such as migrations, information and fake news diffusion, opinion polarisation, social and mental well-being well-being (ii) designing new data-centric services which exploit knowledge about the extracted social network structures. Successful candidates will be supervised by Dr. Marco Conti and Dr. Andrea Passarella. The PhD/Post-doc activities will involve interdisciplinary approaches focusing on a mix of (i) efficient data crawling and collection techniques, (ii) large- scale data analysis, (iii) knowledge extraction, (iv) design of data-centric services in OSN platforms. Candidate profile ----------------- Ideal candidates should have or about to obtain a MSc degree (for the PhD position) or PhD degree (for the Post-doc level) in Computer Science, Computer Engineering, Physics, Statistics, or closely related disciplines, and a proven track record of excellent University grades (PhD position) and or of publications in relevant top-tier conferences and journals (Post-doc position). Preferably, the topic of the MSc/PhD thesis should have been in one of the relevant research areas (BigData analytics, OSN analysis/programming, Complex network analysis). Good written and spoken communication skills in English are required. ================================================= Position #2: Distributed Artificial Intelligence at the edge of the Internet ------------------------------------------------------------------------- Position type: Post-doc or PhD Job description --------------- The expected amount of data generated by pervasive devices at the edge of the network (personal mobile devices, IoT devices, devices in industrial environments) calls for new distributed machine learning approaches, which depart from the conventional model of collecting all data in huge data centres where machine learning models are used to extract knowledge. Instead, data analytics is performed on small datasets collected by individual nodes, possibly with limited computation/networking/storage resources, which then collaborate to learn more complex models. This approach is currently explored, among others by Google in the Federated Learning activity (https://research.google.com/pubs/pub44822.html). Network effects arising from the collaboration of multiple devices, under the control of individual users, become an additional dimension in the AI process of extracting knowledge from data, which has not been widely explored so far. Such a user-centric approach to AI promises to be more scalable, and to better preserve the users' privacy (or the confidentiality of the data, e.g., for industrial applications), with respect to centralised AI approaches. The PhD/Post-doc activities will be focused on the design and evaluation of distributed AI algorithms to be implemented on collaborating sets of networked nodes. Distributed deep learning for edge environments will be a specific subject of investigation. Successful candidates will be supervised by Dr. Marco Conti and Dr. Andrea Passarella. The PhD/Post-doc will work on a mix of these topics: (i) design and prototyping of distributed, privacy/confidentiality-preserving AI algorithms for edge environments, possibly in presence of resource-constrained devices; (ii) evaluation of the performance (e.g., with respect to centralised solutions, in terms of accuracy and generated network traffic); (iii) analysis of the performance bounds of the distributed analytics algorithms Candidate profile ----------------- Ideal candidates should have or about to obtain a MSc degree (for the PhD position) or PhD degree (for the Post-doc level) in Computer Science, Computer Engineering, Mathematics, or closely related disciplines, and a proven track record of excellent University grades (PhD position) or of publications in relevant top-tier conferences and journals (Post-doc position). Preferably, the topic of the MSc/PhD thesis should be in one of the relevant research areas (IoT, mobile networking and computing, machine learning, deep learning, BigData analytics). Good written and spoken communication skills in English are required. ================================================= Position #3: Internet of People ------------------------------------------------------------------------- Position type: Post-doc or PhD Job description --------------- The cyber-physical convergence, the fast expansion of the Internet at its edge, and tighter interactions between human users and their personal mobile devices push towards a data-centric Internet where the human user becomes more central than ever. We argue that this will profoundly impact primarily on the way data should be handled, processed and anlysed in the Next Generation Internet. It will require a radical change of the Internet data-management paradigm, from the current platform-centric to a human-centric model. This will also impact on the way knowledge is extracted from data, primarily via AI techniques, which will become more distributed, collaborative (among users' devices) and human-centric, as human personal devices will be prime nodes where local data will be analysed. We call this new paradigm the Internet of People (IoP) because it embeds human behaviour models in its algorithms. To this end, IoP algorithms exploit quantitative models of the humans' individual and social behaviour, from sociology, anthropology, psychology, economics, physics. IoP derives from research ideas recently described by members of the group in (e.g., [1,2]) and around which an international research community is gathering [3]. The Post-doc/PhD activities will be focused on the investigation of the IoP paradigm, along several possible directions. Possible topics include (but are not limited to): (i) the definition and characterisation of the IoP graph, namely a multi-layer graph capturing both opportunities for data exchange between nodes (e.g., via the legacy Internet, via self-organising networking), as well as properties such as the social relationships between users' that impact data exchange between their devices; (ii) the definition of the IoP data-management primitives and algorithms, i.e. the definition of a minimal set of primitives for data management in IoP, and the corresponding protocols to implement them, to provide higher-level services to applications; (iii) human-centric resource management in IoP, i.e., the way users' personal devices will decide how to share resources with each other to collaboratively realise data-management services; (iv) data ownership, sharing and control in IoP, i.e., how IoP could exploit systems such as Personal Data Stores to guarantee users-controlled access to their data. In all cases, activities will also include performance evaluation of the IoP protocols/algorithms, via modelling and large-scale simulations. A common feature in all activities will be embedding models of the human behaviour in the IoP primitives/algorithms, to make IoP personal devices (the nodes of the IoP graph) behave as proxies of their human users in IoP. To this end, models coming from sociology, anthropology, micro-economics might be used. Examples of this approach developed by the group can be found, e.g., in [4,5,6]. Successful candidates will be supervised by Dr. Marco Conti and Dr. Andrea Passarella. [1] M. Conti, Andrea Passarella, Sajal K. Das, "The Internet of People (IoP): A new wave in pervasive mobile computing", Perv. Mob. Comp., 2017 [2] Marco Conti, Andrea Passarella, "The Internet of People: A human and data-centric paradigm for the Next Generation Internet", Computer Communications 131: 51-65 (2018) [3] E.M. Belding et al., Internet of People, Dagstuhl Sem. 17142, 2017 [4] Marco Conti, Matteo Mordacchini, Andrea Passarella: Design and Performance Evaluation of Data Dissemination Systems for Opportunistic Networks Based on Cognitive Heuristics. ACM Trans. Aut. Adapt. Sys. 8(3): 12:1-12:32 (2013) [5] V Arnaboldi, M Conti, A Passarella, RIM Dunbar, “Online Social Networks and information diffusion: The role of ego networks”, Elsevier Online Social Networks and Media 1 (2017), 44-55 [6] V. Arnaboldi, A. Passarella, M. Conti, R.I. Dunbar, Online Social Networks: Human Cognitive Constraints in Facebook and Twitter Personal Graphs, Elsevier, 2015, ISBN 9780128030424. Candidate profile ----------------- Ideal candidates should have or about to obtain a MSc degree (for the PhD position) or PhD degree (for the Post-doc level) in Computer Science, Computer Engineering, or closely related disciplines, and a proven track record of excellent University grades (PhD position) and or of publications in relevant top-tier conferences and journals (Post-doc position). Preferably, the topic of the MSc/PhD thesis should be in one of the relevant research areas (e.g., mobile networking and computing, Future Internet architectures, data-centric Internet, complex networks/network modelling applied to Internet systems). Familiarity with inter-disciplinary approaches to Internet systems is a plus. Good written and spoken communication skills in English are required. Research group -------------- The PhD students will work in the Ubiquitous Internet group of IIT-CNR in Pisa, Italy (http://cnd.iit.cnr.it). UI activities range over multiple topics related to the design and analysis of Future Internet networking and computing systems, including data-centric networks, mobile cloud, distributed AI, online/mobile social networks, self-organising networks, hybrid wireless/wired networking and computing. The UI group has a strong track record of successful activities in European projects, from FP6 to H2020, which is reflected in the many international collaborations in EU and USA activated by the researchers of the group. Application procedure --------------------- Applications should consist of (all documents in English): - a complete CV, including exams taken during the University degrees (including the MSc final degree), with grades, and a link to the MSc. thesis - a 1-page research statement showing motivation and understanding of the topic of the position - at least one contact person (2 even better) who could act as reference(s) The applications and any request of information should be sent to: a.passarella@iit.cnr.it, with subject, respectively (replace "PhD" with "Post-Doc" as appropriate): "PhD application: Analysis of large-scale Online Social Networks" "PhD application: Distributed AI at the edge of the Internet" "PhD application: Internet of People" Contact point ------------- For any additional information or clarification, please send a message to a.passarella@iit.cnr.it