PhD/postdoc Scouting Spring 2023

The Ubiquitous Internet Research Unit of IIT-CNR (Pisa, Italy) is scouting for talented candidates for PhD and post-doc research areas, in view of possible recruiting in the coming months. Many of the areas are linked to the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU, in the framework of the following projects:

• FAIR: Extended Partnership on Foundational aspects of Artificial Intelligence
• RESTART: Extended Partnership on Telecommunications of the Future
• NQSTI: National Quantum Science and Technology Institute
• MOST: National Competence Center for Sustainable Mobility
• THE: Tuscany Health Ecosystem

For all areas and topics, Expressions of Interest should be sent by 12 April 2023 following the instructions provided below

Specifically, Expressions of Interest are solicited in the following areas:

1. Human-centric Artificial Intelligence. Three positions are open on the following topics:
   • Modelling and optimisation of collaboration between humans and AI in hybrid human-AI systems
   • Causality-aware AI and causal experiments in human-centric pervasive systems
   • BigData analysis in Online Social Networks for a Human-centric Metaverse
   • Robustness of decentralised AI systems

These positions are supported by:

• FAIR: Extended Partnership on Artificial Intelligence (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

1. Future Internet. Five positions are open for the following topics:
   • The Internet of Metaverse (IoM): A Virtual Augmentation Paradigm for the Next Generation Internet
   • Energy-efficient Green AI for future pervasive Internet
   • Efficient FaaS orchestration and scheduling for edge AI
   • End-to-end network slicing and edge resource orchestration in beyond 5G/6G networks
   • Computing workload optimisation in industrial networks

These positions are supported by:

• RESTART: Extended Partnership on Telecommunications of the Future (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

1. Quantum Internet: One position is open on the following topic:
   • Network/compute infrastructure management with quantum communications 

This position is supported by:

• NQSTI: Extended Partnership on National Quantum Science and Technology Institue (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

1. 6G vehicular networks. One position is open on the following topic:
   • Cross-layer network design for 6G vehicular networks

This position is supported by:

• MOST: National Competence Center on Smart Mobility (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

1. Digital health.   One position is open on the following topic:
   • Digital phenotyping and m-health systems

This position is supported by:

• THE: Tuscany Health Ecosystem (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

While these topics are the priority, we may also consider expressions of interest for other areas, listed as “continuous” https://turig.iit.cnr.it/ui-positions/

Human-centric Artificial Intelligence

Abstract

The next wave in AI evolution posits that future AI systems will be able to interact and collaborate with humans, to perceive and act within evolving contexts, all this while being aware of their own limitations, capable of adapting to new situations and of interacting appropriately in complex social settings. All these goals fall under the umbrella of human-centric AI. 

The three topics open in this area are illustrated below.

Topic 1.1 Modelling and optimisation of collaboration between humans and AI in hybrid human-AI systems

AI systems are increasingly moving from a centralised, black-box approach to more decentralised approaches where "smaller" AI systems operate closer to the final users, possibly also on their own devices, and interact with each other, interpreting and anticipating human-users’ behaviour. We are looking for a PhD/post-doc student working on mixed human-AI systems, whereby it would be possible to automatically decide how to shift responsibilities between humans and AI agents, possibly via higher-level AI which predicts at each point in time the best option for a specific task. The activities will involve a mix of modelling, systems/algorithms design, prototype development, and performance evaluation via experiments, analysis, and simulation.

Supported by: FAIR, Extended Partnership on Artificial Intelligence (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

Topic 1.2 Causality-aware AI and causal experiments in human-centric pervasive systems

We want to move from learning correlations (as traditional, associations-based, machine-/deep-learning approaches do) to learning causal relationships.  To this aim we propose to establish a synergy between a network of heterogenous electronic devices (smartphones, wearables, IoT devices, virtual assistants, etc.) and causal explainable AI, leveraging our hyperconnected environments to set up causal experiments and deploy the next wave of decentralized human-centric causal intelligent learning. The target use cases will be centered around pervasive systems. The research activities will involve theoretical modelling, systems/algorithms design, performance evaluation via experiments, analysis, simulation, according to the expertise of the candidate.

Supported by: FAIR, Extended Partnership on Artificial Intelligence (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

Topic 1.3 BigData analysis in Online Social Networks for a Human-centric Metaverse 

The cyber and physical worlds are increasingly becoming indistinguishable. This is fostered by enabling technologies such as IoT and pervasive networks, advanced data management and analytics techniques, and advanced platforms with massive diffusion, chiefly among them Online Social Networks (OSN). Whatever we do in one world has immediate consequences on the other world, thanks to a constant flow of data - and online analytics - between the two worlds. In this context, the vision of the Metaverse provides additional perspectives, augmenting human interactions with things and other humans across the two worlds. The role of the humans in this socio-technical complex system is key, and still largely unexplored. The goal of this PhD/postdoc topic is to characterise the individual and social behaviour of humans, using OSN as “big data microscopes” and artificial intelligence as a tool to carry out this characterization. The design of novel technical solutions to support human-centric approaches to the evolution of OSN in the perspective of the Metaverse may also be part of the activities. The activities will involve a mix of data collection/analysis/visualization, applied artificial intelligence, algorithms design, data-driven evaluation via experiments, analysis, simulation.

Supported by: FAIR, Extended Partnership on Artificial Intelligence (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

Topic 1.4 Dynamic & Robust decentralised AI

AI systems are increasingly decentralised, whereby groups of users/nodes in a network keep their data local, train local AI models based on them, and finally collaborate to train a more complex model based on the ones trained locally. This is typically an iterative process, through which, for example, nodes exchanged local models with their immediate neighbours, and aggregate the models received from neighbours according to specific rules. The goal of the activities in this position are to study specifically (i) the dynamic dimension of this process and (ii) the robustness of this process. With respect to point (i), the subject lies in understanding how to adapt Continual Learning approaches to work in decentralised settings, to cope with the fact that data evolve over time, new data become available at nodes, and old data may become stale and not useful anymore. With respect to point (ii), the objective is to understand the effect of malicious behaviour in such decentralised systems, e.g., how decentralised learning reacts to (and can be protected against) injection of false or malicious nodes/local models designed to drive the overall system towards specific outcomes. The PhD/postdoc may work either in (i) or (ii), or at the intersection of these topics.

Supported by: FAIR, Extended Partnership on Artificial Intelligence (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

The activities above will also establish strategic partnerships with one or more of following European projects where IIT is involved: CHIST-ERA SAI (Social Explainable AI, https://www.sai-project.eu/), HumaneAI-Net (https://www.humane-ai.eu/), SoBigData++ (https://plusplus.sobigdata.eu/).

Candidate profile

Ideal candidates should have or about to obtain a MSc degree (for applying at the PhD level) or a PhD (to apply at the post-doc level)  in Computer Science, Computer Engineering, Mathematics, or closely related disciplines, and a proven track record of excellent University grades (PhD level) or scientific publications (post-doc level). Preferably, the MSc/PhD should be in one of the relevant research areas:

• Topic 1.1: Artificial Intelligence, BigData analytics, distributed systems, Probability & Statistics
• Topic 1.2: Probability & Statistics, Artificial Intelligence, BigData analytics. Previous knowledge of causality theory is a plus
• Topic 1.3: Artificial Intelligence, BigData analytics, Probability & Statistics
• Topic 1.4: Artificial Intelligence, BigData analytics, distributed systems, Probability & Statistics

Good written and spoken communication skills in English are required.

Contacts

Andrea Passarella andrea.passarella@iit.cnr.it

Scholar profile https://scholar.google.com/citations?user=sesKnygAAAAJ

Marco Conti marco.conti@iit.cnr.it

Scholar Profile https://scholar.google.com/citations?user=KniFTD0AAAAJ

Chiara Boldrini chiara.boldrini@iit.cnr.it

Scholar profile: https://scholar.google.com/citations?user=rCrzxbsAAAAJ

Lorenzo Valerio lorenzo.valerio@iit.cnr.it

Scholar profile: https://scholar.google.com/citations?user=CjoX4voAAAAJ

Future Internet

Abstract

The Future Internet will be an exceptionally complex environment where the focus will gradually move from increasing the speed of transferring bits, to the efficiency of supporting advanced services from within the network. Users' devices being pervasive, the future Internet paradigms must consider users at the centre of the technical design, and Internet protocols must consider models of their users’ behaviour into account to dynamically optimise their operations. The following topics address the many technical challenges implied by this novel vision on the Internet evolution.

Topic 2.1 The Internet of Metaverse (IoM): A Virtual Augmentation Paradigm for the Next Generation Internet

Scientific advances in the fields of digital twinning, extended reality, the Internet of People and the Internet of Senses, as well as tighter interactions between the physical and the cyber world, push towards a Metaverse implementation where the current notion of cyber-physical world is extended to a much richer set of concepts, and where the traditional Internet layers are augmented to capture the social avatar transformation of the users. We argue that the current Internet paradigms, which are infrastructure-centric, people-centric and senses-centric are not enough to cope with such an emerging scenario with a wider range of applications. This calls for a radically new Internet paradigm, that we name the Internet of Metaverse (IoM), where the physical users, their cyber avatars and their cyber-physical social relationships are not seen merely as simplistic parameters of Metaverse applications, but become active elements of the Internet composition.

Supported by: RESTART: Extended Partnership on Telecommunications of the Future (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

Topic 2.2 Energy-efficient Green AI for future pervasive Internet

The future Internet will not be a set of dumb pipes designed to carry bits at the highest possible speeds. Data-centric services will be core components of the Internet fabric. Embedded AI will be pervasive in all devices of the future Internet, including on devices normally considered “terminals”, such as smartphones, tablets, edge devices and (Industrial) IoT. In this vision, AI algorithms cannot be simply moved from big data centers to small pervasive devices, but they have to be redesigned to cope with limited resources. In this topic we specifically look at the energy efficiency of pervasive AI, i.e., at how to make decentralised AI efficient from an energy standpoint. Examples are novel approaches to reduce the footprint of training complex AI models on energy-powered devices, as well as the energy efficiency aspects of decentralised AI algorithms running on edge devices (e.g., the energy footprint of communication resulting from nodes exchanging data/models where training collaboratively complex AI models).

Supported by: RESTART: Extended Partnership on Telecommunications of the Future (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

Topic 2.3 Efficient FaaS orchestration and scheduling for edge AI

Function-as-a-Service (FaaS) is a programming model that simplifies the cloud-native developers’ job while enabling serverless computing in the backend, with its promises of infinite scalability, fully-automated configuration, and flexible billing. Due to its advantages, FaaS platforms are also being explored in edge systems, but such environments create significant challenges that the research community has only started addressing. Cognitive applications are of definite interest because they are dominant in Internet of Things (IoT) scenarios, which thrive at the edge due to reduced latency/traffic and improved isolation/privacy. Under this topic, the research will focus on the real-time orchestration of serverless computing infrastructures with resource-constrained devices and on the optimized scheduling of remote FaaS call invocations, with both stateless and stateful workflows. Integration with existing serverless frameworks, such as Apache OpenWhisk or OpenFaaS, and 5G/6G virtual network function platforms will be sought. Furthermore, the open-source project ServerlessOnEdge (https://github.com/ccicconetti/serverlessonedge), using Google’s gRPC for inter-process communication, will be considered as a first step towards achieving the topic objectives..  

Supported by: RESTART: Extended Partnership on Telecommunications of the Future (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU).

The activities above will also establish strategic partnerships with Horizon Europe projects EDGELESS (https://www.edgeless-project.eu/) and 6Green (http://www.6green.eu/) where IIT is involved.

Topic 2.4 End-to-end network slicing and edge resource orchestration in 6G networks

Future generation mobile networks are expected to converge towards highly distributed systems natively embedding (edge) computing and storage resources, which will serve diverse applications with demanding QoS requirements, such as ultra-low latency and ultra-high reliability. Network function virtualisation and network slicing are key technology enablers  as they allow to build dedicated network instances (virtual networks) over a common physical network, which are tailored to the application or communication services. However, existing centralised management and orchestration approaches pose critical limitations in terms of scalability, flexibility, and sustainability. In this topic we aim to investigate novel zero-touch management & orchestration approaches, with native AI integration, to support automated service provision and resource orchestration of extremely heterogeneous and dynamic end-to-end network and compute slices that cross multiple technological domains (i.e., device, RAN, core, edge, and cloud) in highly dynamic and uncertain environments. A special emphasis will be dedicated to solutions that can promote energy efficiency across the entire network.

Supported by: RESTART: Extended Partnership on Telecommunications of the Future (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

Topic 2.5 Computing workload optimisation in industrial networks

The heterogeneity of the computing elements and industrial communication methodologies (wired and wireless, such as TSN and TSCH) in terms of computation speed, memory capacity, energy constraints and latency requirements many times leads to fragmented end-to-end industrial networking workload management. The emerging paradigm of edge computing has revolutionised industrial Internet of Things applications, delivering computational power closer to the field sensors, actuators and machines. Consequently, both traditional and AI tasks, data and models, typically existing in the cloud or fog layers, can now be located on the edge or mobile devices as well. While the inherent flexibility of such distributed approaches has drawn considerable attention, a thorough investigation on their resource consumption footprint under strict industrial use case constraints and optimisation objectives is still missing. Under this topic, the research will focus on the distributed resource optimization of heterogeneous industrial computing infrastructures, focusing on characteristic computation problems arising in interconnected industrial deployments with stationary and/or mobile computing artefacts.

Supported by: RESTART: Extended Partnership on Telecommunications of the Future (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

The activities above will also establish strategic partnerships with one or more of following European projects where IIT is involved: SLICES Research Infrastructure (https://slices-ri.eu/), 6Green (https://www.6green.eu/), MARVEL (https://www.marvel-project.eu/), RE4DY (https://re4dy.eu/).

Candidate profile

Ideal candidates should have or about to obtain a MSc degree (for applying at the PhD level) or a PhD (to apply at the post-doc level) in Computer Science, Computer Engineering, Telecommunications Engineering, or closely related disciplines, and a proven track record of excellent University grades (PhD level) or scientific publications (post-doc level). Preferably, the MSc/PhD should be in one of the relevant research areas:

• Topic 2.1: Distributed Systems, Internet technologies
• Topic 2.2: Artificial Intelligence, Distributed Systems 
• Topic 2.3: Distributed Systems, Cloud-native architectures, frameworks, and tools
• Topic 2.4: Mobile Networks, Distributed Systems, Artificial Intelligence. Previous knowledge of network virtualization and orchestration technologies is a plus.
• Topic 2.5: Distributed Computing, Artificial Intelligence, Industrial Networking

Good written and spoken communication skills in English are required.

Contacts

Andrea Passarella andrea.passarella@iit.cnr.it

Scholar profile https://scholar.google.com/citations?user=sesKnygAAAAJ

Marco Conti marco.conti@iit.cnr.it

Scholar Profile https://scholar.google.com/citations?user=KniFTD0AAAAJ

Raffaele Bruno raffaele.bruno@iit.cnr.it 

Scholar profile: https://scholar.google.com/citations?user=sjN4vKkAAAAJ

Claudio Cicconetti claudio.cicconetti@iit.cnr.it 

Scholar profile: https://scholar.google.com/citations?user=sTVmHWUAAAAJ

Theofanis Raptis theofanis.raptis@iit.cnr.it

Scholar profile: https://scholar.google.com/citations?user=aDoDo_kAAAAJ

Quantum Internet

Abstract

The application of quantum systems to computing and communication is pushing research into a new era of unlimited possibilities. The Quantum Internet will accelerate the development of quantum computing applications through a pooling of the (initially scarce and dispersed) quantum compute resources. At the same time, novel distributed consensus schemes and unconditionally secure communications through Quantum Key Distribution (QKD) will advance the features offered by classical communication/computation infrastructures, leading to novel exciting opportunities. However, the nascent quantum networks have several research challenges ahead. These include an inherent fragility of communications, relatively limited capabilities, sparse deployments due to an incremental adoption of technologies in the field, and difficulty of interoperability due to the lack of best practices and standards in the area.

Topic 3.1 Network/compute infrastructure management with quantum communications

Modern cloud-edge applications rely on a micro-service architecture, where the software complexity is broken down in many elementary functions, each offered by a dedicated service. The integration of QKD network elements (devices, trusted relay nodes, key management systems, etc) with edge-cloud architectures is still ongoing and will be addressed in this topic. Specifically, progress beyond state-of-the-art will be sought along different directions: support of Software Defined Networking (SDN) architectures; commoditization of resources for QKD-enabled unconditional security and edge-cloud computation offloading through joint allocation/scheduling; exploration of the serverless paradigm, which foresees a stateless execution of elementary functions that are not bound to a specific computation node; long-term advancements of quantum networks through Measurement Device Independent (MDI) stations or quantum repeaters, which relax the assumption on intermediate relay nodes being trusted.

Supported by: NQSTI: Extended Partnership on National Quantum Science and Technology Institue (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

The activity above will also establish strategic partnerships with the following European/national projects where IIT is involved: Quantum Italy Deployment (QUID), Development of Quantum Systems and Technologies for Unconditionally Secure Communications (QUANCOM).

Candidate profile

Ideal candidates should have or about to obtain a MSc degree (for applying at the PhD level) or a PhD (to apply at the post-doc level) in Computer Science, Computer Engineering, Telecommunications Engineering, Mathematics, Physics, or closely related disciplines, and a proven track record of excellent University grades (PhD level) or scientific publications (post-doc level). Preferably, the MSc/PhD should be in one of the relevant research areas: quantum communications, quantum network architectures and protocols, management of classical communications/computation infrastructures.

Knowledge of quantum computing, though preferable, is not a prerequisite for application. Good written and spoken communication skills in English are required.

Contacts

Andrea Passarella andrea.passarella@iit.cnr.it

Scholar profile https://scholar.google.com/citations?user=sesKnygAAAAJ

Marco Conti marco.conti@iit.cnr.it

Scholar Profile https://scholar.google.com/citations?user=KniFTD0AAAAJ

Claudio Cicconetti claudio.cicconetti@iit.cnr.it 

Scholar profile: https://scholar.google.com/citations?user=sTVmHWUAAAAJ

Cross-layer network design for 6G vehicular networks

Vehicular communications (V2V, V2X) and networking have the potential to provide a relevant contribution to the increase of the sustainability and quality of life in urban and suburban areas, by better control of road traffic, thus reducing CO2 emissions, oil consumption, and battery drainage (for electric powered vehicles), and an increase in people safety, including vehicles’ passengers and pedestrians. Efficiency, in terms of both energy consumption and spectrum use, and reliability of vehicular communication and networking solutions can be significantly increased by introducing new networking paradigms, which combine traditional communications in the sub-10-GHz bands (e.g.., cellular networks and WiFi), with emerging wireless communication technologies. Particularly, visible light communications (VLC) and intelligent reflecting surfaces (IRS) are attractive from the energy efficiency standpoint: VLC exploit, as transducers, infrastructures already present in urban vehicle scenarios (traffic lights, car lights, lighting elements in public spaces, etc…), whereas IRS are passive elements (i.e. they do not consume energy) yet able to boost the channel quality between transmitters and receivers. Moreover, VLC provides an impressive bandwidth compared to traditional wireless communication technologies. Combining these technologies in 6G networks, by also taking into account specific application-level requirements, allows the development of powerful tools to improve the capabilities, efficiency, and reliability of wireless networks for vehicular environments.

Supported by: MOST: National Competence Center on Smart Mobility (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)

Candidate profile

Ideal candidates should have or are about to obtain an MSc degree (for the PhD level) or PhD degree (for the Post-doc level) in Computer Science, Computer Engineering, Communications Engineering, Mathematics, or closely related disciplines, and a proven track record of excellent University grades (PhD level) or of publications in relevant top-tier conferences and journals (Post-doc level). Preferably, the topic of the MSc/PhD thesis should be in one of the following areas

• multi-objective optimization algorithms, statistics and probability, simulations, prototyping/testing

Good written and spoken communication skills in English are required.

Contacts

Loreto Pescosolido loreto.pescosolido@iit.cnr.it

Scholar profile: https://scholar.google.it/citations?user=ixkn5rAAAAAJ&hl=it

Andrea Passarella andrea.passarella@iit.cnr.it

Scholar profile: https://scholar.google.com/citations?user=sesKnygAAAAJ&hl=it&oi=ao

Emilio Ancillotti emilio.ancillotti@iit.cnr.it

Scholar profile: https://scholar.google.it/citations?user=IfL80FkAAAAJ&hl=it

Digital phenotyping and m-health systems

Digital phenotyping refers to the analysis of heterogeneous information derived from personal and wearable devices aimed at characterizing individual human beings and their health status. It can include behavioral patterns, sleep patterns, social interactions, physical mobility, nutrition, cognitive functioning, speech production and many others, depending on the individual health profile. m-health applications, integrated with IoT systems, represent an important data source for digital phenotyping, but they should be also enriched with AI algorithms to automatically detect specific health conditions, risky and adverse situations, and to implement personalized interventions. To this target it is essential to collect real-world data through pilot studies in order to evaluate and validate the proposed solutions in real scenarios. For this reason, the activity can also require the development/integration of a m-health system prototype with novel wearable sensors and devices, and to actively participate in experimental campaigns. Another focus of the activity is also the integration of AI algorithms in the m-health system through on-device machine learning tools to guarantee the processing of sensitive data directly on the personal mobile device, and the analysis of explainable AI solutions to appropriately support the system’s output with interpretable motivations. The research will be conducted in collaboration with medical units. 

Supported by: THE: Tuscany Health Ecosystem (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU

Candidate profile

Candidates should have or about to obtain a MSc degree (for the PhD level) or PhD degree (for the Post-doc level) in Computer Science, Computer Engineering,  Biomedical Engineering, or closely related disciplines, and a proven track record of excellent University grades (PhD level) or of publications in relevant top-tier conferences and journals (Post-doc level). Preferably, the topic of the MSc thesis should be in one of the relevant  research areas:

• e-health/m-health applications, Artificial Intelligence, BigData analytics, distributed systems.

Good written and spoken communication skills in English are required.

Contact

Franca Delmastro – franca.delmastro@iit.cnr.it

Scholar profile: https://scholar.google.com/citations?hl=it&user=fiw73vIAAAAJ

Ubiquitous Internet Research Unit

The Ubiquitous Internet RU is part of the Institute of Informatics and Telematics of CNR, located in Pisa, Italy. Pisa hosts the biggest CNR campus in Italy, and is the home of three prestigious Universities, the University of Pisa, Scuola Normale Superiore, S. Anna School of Advanced Studies. Pisa research institutions have a long heritage of excellence in Computer Science, dating back from the first Italian computer (CEP, 1961) and the first Italian connection to the Internet (1986).

The activities of the Ubiquitous Internet Research Unit range over multiple topics related to the design and analysis of the Next Generation Internet (NGI) networking and computing systems, including edge computing, Internet of People, decentralised AI, human-centric networked systems, online/mobile social networks, data centric and self-organising networks.  Verticals of interest include Industry 4.0, digital health, energy efficiency, smart mobility. The RU has a strong track record of successful activities in national and European projects, from FP6 to Horizon Europe (HE), which is reflected in the many international collaborations in EU and USA activated by the researchers of the RU.

Currently active reference projects include:

• AI and BigData: H2020 HumanAI-Net, SoBigData++, CHIST-ERA SAI (Social Explainable AI)
• Edge computing & decentralised AI: HEU RE4DY, H2020 MARVEL, 
• BigData, AI & Next-Generation Internet Infrastructures: ESFRI-SLICES, ESFRI SoBigData, HEU SLICES-PP, HEU SoBigData-PPP, H2020 SLICES-DS, SLICES-SC, SoBigData++
• Quantum Computing & Networking: H2020 HPCQS, PON-MIUR QUANCOM
• PNRR projects: FAIR (topic: foundations of AI), SoBigData.it (topic: BigData analytics and beyond 5G pervasive networks), MOST (topic: sustainable mobility), RESTART (topic: beyond 5G/6G networks), NQSTI (topic: quantum Internet), ICSC (topic: BigData analytics and quantum computing), THE (topic: Digital health solutions), SERICS (topic: Misinformation and Fakes)

Please refer to the publication records of the contact persons for more specific information.

Alumni

Over the years, the UI research unit has hosted several successful PhDs and Post-docs, who are now working at prestigious institutions and industries. Below a shortlist of the most recent alumni:

• Valerio Arnaboldi (Sternberg Lab, Caltech, USA)
• Simone Bolettieri (Nokia Bell Labs, Belgium )
• Dima Karamschuk (Facebook, London)
• Massimiliano La Gala (Findomestic, Italy)
• Haitam Laarabi (Berkeley Lab, US)
• Antonino Masaracchia (Queen’s University Belfast, Ireland)
• Davide Mascitti (Bending Spoons, Italy)
• Maddalena Nurchis (Socialpoint, Spain)
• Pavlos Paraskevopoulos (Baresquare, Greece)
• Fabio Pezzoni (Ocado Technology, Spain)
• Mustafa Toprak (Kellify, Italy)
• Pantelis Vikatos (Orfium, USA)
• Gevorg Yeghikyan (PerigonAI, UAE

Expressions of interest

If interested in discussing one of the described topics, please send to the provided contact person(s):

• a complete CV, including, for the PhD level, the 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
• at least one (PhD level) or two (Post-doc level) contact person(s) who could act as reference(s)

This material, and any request of information, should be sent to the reference contact point(s) for the specific topic, with subject, respectively: "PhD scouting: <topic>", or "Post-doc scouting: <topic>", where <topic> is the title of the topic you are interested. Applications for more topics are allowed. Although we encourage candidates to apply for specific topics, they may also express their interest for an entire area (in case more topics are described in a single area).

Expressions of interest will be continuously considered upon reception. The very final date for sending EoIs is 12th April 2023, but topics may be closed earlier, depending on the received EoIs. Interested people are strongly encouraged to send expressions of interest as soon as possible. Specific deadlines, when applicable, are indicated in the following.

Information for all PhD topics

IIT-CNR is part of several PhD programs including:

• the National PhD program in Artificial Intelligence (https://www.phd-ai.it/) co-organised by CNR, University of Pisa, Polytechnic of Turin, Campus Biomedico University of Rome, University of Naples “Federico II”, Sapienza University of Rome;
• the PhD programs in Computer Engineering (https://phd.dii.unipi.it/en/) and Computer Science (https://dottorato.di.unipi.it/), hosted by the University of Pisa
• the PhD program in Smart Computing, jointly organised by the Universities of Florence, Pisa, Siena, CNR, FBK (https://smartcomputing.unifi.it/)
• the PhD program of the IMT School for Advanced Studies of Lucca (https://www.imtlucca.it/en/programma-dottorato/phd/systems-science)

A subset of the received expressions of interest will be invited to apply to one of these PhD programs. The next calls are expected with deadlines in Summer 2023. The selection for PhD programs is managed by the related hosting university and candidates will apply directly to the official call of the specific PhD.

The salary for PhD positions is approx. 1100 EUR/month (tax exempt).

For all topics, it will be possible (and advised) to organise one visiting student period abroad (typically, 6 months) during the PhD.

Deadlines: interested candidates are required to contact the reference person as soon as possible. Selected candidates will have to formally apply to the official call of the respective Universities.

Information for all Post-doc topics

IIT regularly opens formal calls for post-doc positions . The topics described below are among the current and future interests of the Institute, and specifically of the Ubiquitous Internet Research Unit. The scouting process is intended to advertise those topics in view of the calls. It is planned that IIT will formally open post-doc positions shortly, aiming at starting the activities in Q2 2023. Details may vary. 

Moreover, IIT is a member of the ERCIM consortium, which funds post-doc fellowships under the Alain Bensoussain Program (https://fellowship.ercim.eu).

Salaries depend on the expertise of the candidate and the specific post-doc instrument.

Interested candidates are encouraged to contact the reference person for the topic as soon as possible, following the provided instructions.