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ICT International Doctoral School 2020 OPEN CALL – 8 ADDITIONAL PhD Positions

Details

Deadline
Research Field
Formal sciences
Natural sciences
Professions and applied sciences
Funding Type
Funding
Career Stage
22
European Research Programme
Not funded by an EU programme

About

THE ICT INTERNATIONAL DOCTORAL SCHOOL HAS 8 EXTRA PhD POSITIONS!

All the details are available at: https://ict.unitn.it/education/admission/call-for-application

Deadline for applications: 16th June 2020, 4 pm (Italian time, GMT +2)

The application must be completed and submitted by the above deadline, solely by the online system:

https://webapps.unitn.it/Apply/en/Web/Home/dott

THE LIST OF RESERVED TOPIC SCHOLARSHIPS PROVIDED BY FONDAZIONE BRUNO KESSLER IS AVAILABLE BELOW: 

Deep Learning Models for Human Behaviours (1 grant)

This PhD project has the ambition to explore the fusion of multiple modalities and the design of novel cross-modal deep neural network architectures to study social behaviours, social interactions, and human activities In addition, the candidate will work on Generative Adversarial Network (GAN) models to generate realistic human behaviours in a variety of social settings. The ideal candidate will be strongly motivated in developing skills in machine learning with a special focus on deep learning, and in computer vision, multimodal approaches, and human behaviour analysis. The project will be supervised by Bruno Lepri (FBK) and Nicu Sebe (DISI).

Contact: Bruno Lepri lepri@fbk.eu, Nicu Sebe niculae.sebe@unitn.it

 

Robust and Multilingual Hate Speech Detection (1 grant)

The increasing popularity of social media platforms like Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech at scale is still an unsolved problem. In particular, current hate speech detection systems do not perform well on under-resource languages, are not able to generalise well across different platforms, and fail to integrate contextual information (e.g. network structure, discourse context, links to external media). We are therefore looking for candidates with strong interest in hate speech detection using deep learning techniques that would contribute to the development of novel approaches for robust hate speech detection designed to work in multilingual settings with small or no language-specific training data (zero-shot learning).

Contact: Sara Tonelli satonelli@fbk.eu

 

Flexibility and Robustness in Speech Translation (1 grant)

The need to translate the audio from one language into a text in a target language has dramatically increased in the last few years with the growth of audiovisual content freely available on the Web. Current speech translation (ST) systems need to be able to serve different applications working in various scenarios and to satisfy several factors coming from the market (e.g. specific length of the output, adaptation to different domains, real-time processing) or present in the audio (e.g. background noise or strong accent of the speaker). The objective of this PhD is to advance the state of the art in speech translation to make ST flexible and robust to these and other factors. Candidates should have a strong curiosity to solve problems in natural language processing and have a background in deep learning and maths, as well as excellent programming skills in Python. They will work both on theoretical aspects of the problem, and on their practical application in relevant case studies driven by ongoing projects where the MT unit is involved. Applicants are invited to contact us (turchi@fbk.eu and negri@fbk.eu) in advance for preliminary interviews.

Contact: Marco Turchi turchi@fbk.eu, Matteo Negri negri@fbk.eu

 

Data-driven techniques for closed-loop network and service management and RAN disaggregation in 6G Mobile Networks (1 grant)

Although 5G has just arrived, the research towards 6G mobile networks has already started. 5G paved the way towards a connected world where multiple verticals (e.g., automotive, industry, and health), each characterized by different performance targets in terms of bitrate, latency, and reliability, can coexist on the same infrastructure. 6G networks will push this paradigm even further and will require a paradigm shift in the way mobile networks are deployed and operated.

With this fully-funded PhD position, we are looking for a candidate willing to work on cutting edge research in the field of 6G mobile networks with a particular focus on data-driven techniques for closed-loop network and service management and RAN disaggregation (following O-RAN principles).

The successful candidate has obtained a master's degree with excellent marks in computer science, is proficient in networking and programming, has an affinity for algorithm design and artificial intelligence, and enjoys working in a multi-disciplinary project. In particular, evidence of system research experience (that is building your own prototypes to validate fundamental research results) using open-source software like srsLTE and P4 is a strong advantage.

Contact: Roberto Riggio rriggio@fbk.eu

 

Development of innovative Microsystems-based Radio Frequency RF-MEMS passives for next generation of telecommunications, wireless and radio systems (1 grant)

We are looking for a candidate willing to embark a challenging activity focused on the development of innovative Microsystems-based Radio Frequency RF-MEMS passive components and networks for next generation of telecommunications, wireless and radio systems and applications, like 5G and the Internet of Things (IoT). Capitalising on the fully in-house RF-MEMS technology, the candidate will have the opportunity to focus on different stages of prototypes development, from the elaboration of novel RF-MEMS design concepts, to the multi-physical simulation, modelling, fabrication, experimental testing of physical samples and integration.

Contact: Jacopo Iannacci iannacci@fbk.eu

 

SMT-based formal verification of parameterized systems (1 grant)

Embedded systems are a fundamental component of our world. Their ubiquitous adoption and ever-increasing complexity makes the task of verifying their correctness both extremely challenging and extremely important. Techniques based on formal methods and automated theorem proving (particularly in the form of Satisfiability Modulo Theories - SMT) are very appealing in this context, promising to deliver both a higher level of confidence than traditional techniques and a high degree of automation. The objective of this PhD research is that of advancing the state of the art in the application of SMT-based formal methods to parameterized systems -- systems consisting of an unbounded number of components/processes -- which naturally arise in many safety-critical domains. The candidate will work on both theoretical aspects of the problem, as well as its practical applications in relevant case studies, drawn from the domains of railways, avionics and aerospace.

Contact: Alessandro Cimatti cimatti@fbk.eu, Alberto Griggio griggio@fbk.eu

 

Automatic analysis of radar/radar sounder images (1 grant)

We are looking for candidates willing to develop novel methodologies based on machine learning, deep learning, pattern recognition and artificial intelligence for information extraction, classification, target detection and change detection in radar images. The candidate will be requested to deal with images acquired from active systems including Synthetic Aperture Radar (SAR) images acquired from Earth Observation satellite missions, and sub-surface radar sounding data from airborne Earth Observation missions and satellite planetary exploration missions. The latter activity is developed in the framework of the Radar for Icy Moons Exploration (RIME) payload on board of European Space Agency (ESA) JUpiter ICy moons Explorer (JUICE) and Sub-surface Radar Souder (SRS) payload under development for the European Space Agency (ESA) Europe's Revolutionary Mission to Venus (EnVision). Besides the requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are: • master degree in Electrical Engineering, Communication Engineering, Computer Science, Mathematics or equivalents; • knowledge in pattern recognition, image/signal processing, statistic/remote sensing/radar.

Contact: Francesca Bovolo bovolo@fbk.eu

 

Multi-/Hyper-temporal remote sensing image time series analysis (1 grant)

We are looking for candidates willing to develop novel methodologies based on machine learning, deep learning pattern recognition and artificial intelligence for information extraction, classification, target detection and change detection in multi-/hyper-temporal remote sensing images.The candidate will be requested to deal with both multi-/hyper-spectral images acquired by passive satellite sensors and Synthetic Aperture Radar (SAR) images acquired from active systems for Earth Observation. The goal is to design novel methods able to use temporal correlation to model landcover behavior, changes and trends for a better understanding of phenomena over time and of climate change. Besides the requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are: • master degree in Electrical Engineering, Communication Engineering, Computer Science, Mathematics or equivalents; • knowledge in pattern recognition, image/signal processing, statistic/remote sensing, passive/active sensors.

Contact: Francesca Bovolo bovolo@fbk.eu

Organisation

Organisation name
University of Trento
Organisation Country
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