Project Specifc Grants / Reserved Topic Scholarships

B1 - Advanced Computational Effective Strategies for the Optimization-based Design of High-Complexity Electromagnetic System (1 grant) 

The research activity will consist in the study, development, and numerical validation of innovative methodologies based on the suitable integration and customization of optimization approaches, learning-by-examples strategies, and advanced modeling and analysis techniques, for the accurate and efficient design of complex electromagnetic systems and devices for next generation communications and sensing applications.
Contact:  -  

D5 - Development of methodologies for the simulation, processing and analysis of radar data  (1 grant) 

The research activity that will be developed is related to the definition and the development of the Radar for Icy Moon Exploration (RIME) on board the Jupiter Icy Moon Explorer (JUICE) mission of the European Space Agency (see for more details on RIME and  for more details on the JUICE mission). The main goals of the mission are related to the study of the sub-surface of the icy moons of Jupiter (i.e., Ganymede, Europa and Callisto). The research activities related to the PhD position can address different specific directions related to the main problems to be solved for this kind of mission, including the definition, design, implementation and validation of: 1) data simulation techniques for radar performance assessment; 2) radar signal processing techniques; 3) data analysis techniques for the automatic extraction of information from radargrams for the generation of mission products. The PhD student will work at the Remote Sensing Laboratory of the University of Trento.
For more information on the activity of RSLab refer to: or contact

Department of Information Engineering and Computer Science and Department of Physics

D6 - Development of methodologies and automatic techniques for the analysis of Big Data from space(1 grant) 

The research activity that will be developed is related to the definition and the design of techniques for the automatic analysis of Big Data from space. The activities can address different specific topics related to the main problems to be solved for the analysis of the large amount of data that are generally connected with planetary missions. The main expected topics include: 1) deep learning architectures for the automatic classification of space data; 2) data analysis techniques for the automatic extraction of semantic  information from data; 4) data fusion techniques for the integration of multisensor data. These activities can also contribute to the development of the ground segment and the tools for the analysis of the data acquired by the Radar for Icy Moon Exploration (RIME) on board the Jupiter Icy Moon Explorer (JUICE) mission of the European Space Agency (see for more details on RIME and for more details on the JUICE mission). The PhD student will work at the Remote Sensing Laboratory of the University of Trento.
For more information on the activity of RSLab refer to: or contact

Department of Information Engineering and Computer Science and Fondazione Edmund Mach

D7 - Monitoring forest changes with liDAR and SAR data. "ForeLiSA" (1 grant) 

Forests are subject to constant changes, and these changes are connected to natural forests cycle (e.g. growth and death of trees), to anthropogenic disturbances (e.g. logging), and to climatic events (e.g. drought, storms). Remote sensing can be a very powerful tool to map and monitor such changes, in particular LiDAR and SAR data. LiDAR data allow to detect very small changes in the canopy, while satellite SAR data allow to cover areas everywhere in the world. The objective of the project is to use LiDAR and SAR data to monitor different changes in forest environments in different regions of the world, with the final goal to model forest growth at global level with remote sensing data. The specific objectives are: i) to develop tools to model forest growth with multi-temporal LiDAR and SAR data, and their integration; ii) to map forest disturbances with multi-temporal LiDAR and SAR data; iii) to model global forest growth and dynamics with satellite remote sensing data; and iv) to test if forests with higher biodiversity are also more resilient to extreme events. The expected outputs will be to understand the relation among forest growth, forest characteristics, biodiversity, and climatic variables, and to develop new methods to detect changes with remote sensing data and to integrate multi-temporal LiDAR and SAR data.

EIT Digital, Centro Ricerche Fiat S.c.p.a.  and Fondazione Bruno Kessler (FBK)

B5 - Vehicle to Everything: opportunities and constraints in leading the automotive world to 5G  (1 grant) [additional reserved topic scholarship] 

Intelligent Transport Systems (ITS) applications are supported by several communication technologies, each one with its frequency range and specific features. Evaluating the performance of different network options for V2X communication that ensure optimal utilization of resources is a prerequisite when designing and developing robust wireless networks for ITS applications. 5G networks are expected to leverage on virtualization of network resources in order to serve over the same infrastructures applications and services characterized by highly heterogeneous requirements, the so called verticals. The thesis will investigate the potentialities introduced by the 5G network for the automotive domain, identifying use cases and scenarios, and deriving requirements for the M(V)NO. The identified solution will be experimentally validated in a lab environment (Hardware in the loop or simulated scenarios) and in a more realistic conditions.
The scholarship is subject to the acceptance of nondisclosure agreements and the assignment of the outcomes.


D1 - Innovative multiscale approaches for snow parameter identification and monitoring (1 grant) 

The research activity is in the field of satellite remote sensing applied to alpine snow monitoring. The goal is to define, design, implement and validate methods based on pattern recognition and machine learning for the analysis of remote sensing images acquired by different sensors with different resolutions in order to derive relevant snow parameters such as the fractional snow cover, snow water equivalent or the snow duration. The satellite data that will be considered for the research activity will mainly include but are not limited to the images acquired by the ESA Sentinel mission. Different paradigms will be studied related to the most recent methodological developments in the framework of information extraction and multiscale fusion. Specific attention will be devoted to the use of remote sensing data for real applications such as hydrological analysis, ski slopes monitoring, etc.   
Master’s degree or equivalent qualification in telecommunication, mathematics or physic with a strong background in signal processing, remote sensing, and statistical analysis is required. The working language is English. A knowledge of Italian or German would be an asset. Good knowledge of programming languages such as python and/or Matlab is required. 
The Ph.D. student will work mainly at the Institute of Earth Observation of Eurac Research, Bolzano, Italy. In addition, some activities will be carried out at the RSLab of the Department of Information Engineering and Computer Science, Trento. 
The intellectual property of the research results deriving from the activities carried out under the scholarship shall be jointly owned by the parties, in compliance with the provisions of the Grant Agreement GA 730203 PROSNOW.
For more information on the PROSNOW Project, refer to
For more information on the activity of Eurac Research, refer to
Contact: -

FBK - Fondazione Bruno Kessler

(*) All scholarships funded by FBK are subject to the acceptance of the assignment of the ownership of intellectual property of the research results.

A1 - Assessment of health status of patients using Machine Learning (1 grant) (*)

The objective of the work will be to use machine learning methods to establish behaviour and disease models using real datasets from Electronic Health Records (EHR) and behaviour data (such as smartphones and personal trackers). The outcome of the work will be prediction of health status of patients as well as estimation of risk of developing a chronic disease.The ideal candidate will have a background in machine learning, with experience in (or strong willingness to learn) deep learning, applied to high-dimensional, irregular, temporal and sparse data. The post will be part of both, a European Project as well as industrial project, offering an exciting opportunity to work with real patient data as well as having access to an inter-disciplinary team of medical experts, industrial partners and academic partners, both in EU and USA. 

A2 - Robust Dynamic Map Data Recognition and Localization from Large-Scale Crowdsourced Street-Level Imagery (1 grant) (*)

The focus of this PhD position is to research unified methods for holistic understanding of scenes and in particular of street-level environments. Traditionally, such pipelines comprise object recognition algorithms for semantic segmentation or object detection as well as 3d modelling components for infering scene geometry. All these tasks are usually treated independently, which may result in suboptimal overall results for both, object recognition and localization. This PhD course aims at researching unified approaches where object recognition and 3d modeling leverage each other's performance, eventually improving overall object recognition and localization accuracies. To this end, novel machine learning concepts will be developed, prototyped, and scaled to cope with large-scale street-level datasets, demonstrating gains on well-established metrics.    
Contact: -

A3 - Deep learning for image and video analysis (1 grant) (*)

The research will involve novel deep learning-based approaches that will not only improve the current state of the art results in image and video analysis but will be able to cope with the scarcity of available training data. Additionally, the research should investigate the use of multimodal information available in video. The student is expected to publish in the top conferences if computer vision (CVPR/ICCV/ECCV) and multimedia (ACM Multimedia) communities as well as the top journals of the field.

A4 - Deep Learning for Machine Translation (1 grant) (*)

Nowadays, human translation and machine translation are no longer antithetical opposites. Rather, the two worlds are getting closer and started to complement each other. On one side, the evolution of translation industry is witnessing a clear trend towards the adoption of Machine Translation (MT) as a primary support to professional translators. On the other side, the variety of data that can be collected from human feedback provides to MT research an unprecedented wealth of knowledge about the dynamics (practical and cognitive) of the translation process. The future is a symbiotic scenario where humans are assisted by reliable MT technology that, at the same time, continuously evolves by learning from translators activity. This grant aims to transform this vision into reality. The candidate will team up a world-class research effort developing novel MT technology capable to integrate information obtained unobtrusively from real professional translation workflows. Relevant topics include: machine translation, machine learning, deep learning,  sequence-to-sequence models, error analysis,  domain and user adaptation
Contact: -

A5 - Middleware and technologies for real time analysis of IoT streams with machine learning algorithms (1 grant) (*)

The Internet of Things (IoT) is an emerging paradigm that sees millions of sensors and actuators involved in the monitoring and management of systems combining physical machines, devices and humans. The capability to deliver embedded and distributed intelligence allows to provide smarter IoT objects capable to autonomously recognize patterns and environmental conditions and of properly react without the need for centralized control or for human intervention, improving responsiveness and effectiveness of delivered IoT systems. 
The objective of the PhD is the study, analysis and experimentation on middleware and technologies that support the execution of machine and deep learning algorithms on IoT streams providing both short term, closed-loop decision making capabilities at device level, and scalable long term analysis capabilities of a distributed IoT system, leveraging on the different computational capabilities distributed in the various portions of the whole IoT infrastructure, both in the cloud and in the edge of the network (in the IoT gateways and in the IoT devices themselves).                

A6 - Using cross-modal deep learning representations to study human societies (1 grant) (*)

For years, social scientists and policymakers have surveyed populations to collect statistics on demographic characteristics, socio-economic conditions, daily habits, etc. However, given the difficulty of scaling up traditional data collection methods, alternative approaches using novel sources of passively collected data such as social media (text and images), satellites' images, and mobile phone data have been recently proposed.
The current Ph.D. project has the ambition to explore the fusion of multiple modalities and sources of information and the design of novel cross-modal deep neural network architectures to study a broad range of social phenomena such as understanding the socio-economic conditions of city's neighborhoods, the emergence and virality of cultural trends, etc.  
The ideal candidate will be strongly motivated in developing skills in machine learning with a special focus on deep learning, and in computer vision and multimodal approaches.                 

A7 - Modeling and Predicting Social and Financial Wellbeing (1 grant) (*)

The ability of understanding and predicting the social and financial wellbeing for individuals, companies, and societies is of interest to economists, policy designers, financial institutions, and the individuals themselves. Humans have often been described as socio-economic beings given that their financial and economic behavior and, more in general their well-being, is intricately connected with their social behavior. In this project, the goal is merging approaches from network science and machine learning and using data on social interactions, mobility routines and purchase behaviors in order to develop methods for quantify financial and social wellbeing. The Ph.D. project will be conducted within the FBK MobS research unit but with collaborations with the FBK CoMuNe research unit and the Human Dynamics group at MIT Media Lab.                

B2 - Hardware and software solutions for blockchain-based IoT applications (1 grant) (*)

Blockchain can offer IoT devices a playground where they can be identified without the need of involving central trusted authorities (decentralised identity control) and the possibility to operate and interact within a trust-less environment. One of the big challenges of the use of blockchain technologies in combination with IoT is how to provide solutions capable to guarantee at hardware and software level a native support for blockchain and distributed ledger technologies to provide IoT devices with the capability to perform autonomous transactions that result to be intrinsically secure and scalable.
The objective of the PhD is the study, analysis and experimentation of how the combination of hardware, cryptography and software solutions can provide foundations for the creation of infrastructures enabling IoT devices to perform autonomous secured transactions in a trustless environment.               

B3 - Anomaly Detection and Mitigation in Fog Computing (1 grant) (*)

Fog Computing recently emerged as the specialisation of cloud computing to store, manage, and process information close to the edge, where data is actually produced and consumed, to support different IoT applications and use cases that are sensitive to e.g. latency, privacy, bandwidth, etc. Given the amount and heterogeneity of devices, data and applications involved, resilience to anomalies, misconfigurations and security threats is now becoming a major concern for all the involved stakeholders.
The goal of the proposed PhD position is to study and develop novel methods and algorithms for  anomaly detection and mitigation in fog-enabled network environments, with specific attention to balancing high detection accuracy and minimal overhead. Artificial intelligence (e.g. machine learning) and other techniques will support the dynamic detection of anomalies, the runtime execution of security functions, the rerouting of traffic and the reconfiguration of existing policies.          

B4 - Distributed Mobile Edge Computing in software defined 5G systems (1 grant) (*)

Mobile Edge Computing (MEC) provides the ability of effectively process data related to 5G services (e.g. augmented reality, vehicle-to-vehicle communications) at the very edge of the mobile network, by relying on resources dynamically instantiated through solutions based on Software Defined Networking (SDN) and Network Function Virtualization (NFV). This environment is characterised by strict requirements in terms of performance, including latency, bandwidth and more. The thesis will investigate the potentialities introduced by MEC in the 5G domain, identifying use cases and scenarios, and deriving system requirements and deployment models. The identified solution will be experimentally validated in a lab environment (Hardware in the loop or simulated scenarios) and in a more realistic conditions in the CREATE-NET testbe DiVINE.  

C1 - Planning strategies for dialog-based behavior change interventions (1 grant) (*)

The subject of the PhD will focus on the application of planning methods and tools in choosing the most appropriate dialog-based strategies by a virtual coaching system, designed to motivate the user to change his/her lifestyle behavior.   

C2 - Automated planning for adaptive and autonomous systems (1 grant) (*)

Planning is a fundamental capability of systems that are able to adapt their behavior to the changes of the environment in which they operate. For example, a complex factory has to be able to rearrange the production depending on the status of the working stations, and on the batches of products to be completed. Autonomous systems, such as drones or underwater exploratory vehicles, are required to carry out these tasks in full autonomy, harmonizing the achievement of the mission objectives (e.g. surveillance) with the limitations of the available resource (energy, time), and the unpredictability of the environment. The thesis will aim at the development of techniques for planning and scheduling in presence of various forms of uncertainty and non-determinism in a setting where actions are temporally extended. The activity will integrate the combination of formal techniques for model-based verification and automated heuristic-based planning. The studies will be carried out within the Embedded Systems Unit at Fondazione Bruno Kessler. The Unit has a long experience in the application of formal methods in sectors of hardware design, railways, avionics, space. The activity will be part of projects funded by the European Union, the European Space Agency, and major industrial players.

C3/C4 - Automated verification of critical systems (2 grants) (*)

Safety critical systems are becoming increasingly complex. Effective analysis tools are required to detect flaws in theearly design stages, to prove the compliance with requirements, and to ensure the required levels of reliability. The activity will aim at the development of formal techniques for model-based verification and safety assessment of critical systems, including model checking and temporal logics for finnite- and infinite-state systems, Fault Tree Analysis (FTA), Failure Model and Effects Analysis (FMEA), Fault Detection, Identification and Recovery (FDIR). The studies will be carried out within the Embedded Systems Unit at Fondazione Bruno Kessler. The Unit has a long experience in the application of formal methods in sectors of hardware design, railways, avionics, space, and will be part of projects funded by the European Union, the European Space Agency, and major industrial players.

D2 - Remote Sensing Image processing (1 grant) (*)

Remote sensing sensors for Earth observation are experiencing a fast-technological development both in the context of active and passive sensing. Images with enhanced features are available showing better trade-off in terms of spectral, spatial, and temporal resolution. Information extraction and retrieval from such data requires the design, implementation and validation of novel methodologies and algorithms based on pattern recognition, image/signal processing, machine learning and/or data fusion. This has an impact of several application contexts including among the others urban analysis, climate change, precision agriculture, forestry, etc. In the above context, the Remote Sensing for Digital Earth (RSDE) Unit at Fondazione Bruno Kessler is looking for a Ph.D. student candidate willing to work on remote sensing data processing. For further details about RSDE activities please refer to
Besides the general requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are:
• master degree in fields like Electrical Engineering, Communication Engineering, Computer Science, Mathematics or equivalents;
• knowledge in pattern recognition, image/signal processing, statistic and/or remote sensing.

D3/D4 - IoT for Smart Cities and Communities (2 grants)   (*)

The Internet of Things, including smart objects, wearables and wireless sensor networks, is becoming a key technology to enable Smart Cities and Communities. Key scenarios require connecting people and their environments through smart services supported by intelligent devices that observe and provide enriched contextual information. As such devices increasingly pervade physical spaces, challenges of sustainability arise in terms of scalability, device lifetime, and management of the massive amounts of generated data. We approach these challenges starting at the "smart thing" level, exploiting local processing to limit communication and adapting low power wireless communication protocols such as BLE and LoRa to offload information at low cost.    
Motivated by the challenges of Smart Cities and Communities, this research aims to address a range of challenges, to be balanced based on the curriculum of the candidate:
- define the "next smart thing" from an energy efficient embedded systems perspective, considering hardware architecture at system level and considering requirements arising from Smart Communities; 
- explore innovative machine learning approaches to be embedded on resource-constrained devices;
- face the energy efficiency challenge of IoT devices from the perspective of wireless communication, considering standard and non-standard communication stacks.  Preferential background in both signal processing or machine learning, sensor technologies and microcontroller/embedded programming if the candidate is willing to tackle the described research from the point of view of smart sensors/smart computing at the edge. Alternatively experience with wireless technologies, network simulation environment and tools if the candidate is more interested on the wireless communication side.       
Contact: -

D8 - Fusion of remote sensing and citizen science information for geospatial products (1 grant) (*) [additional reserved topic scholarship]

An actual scientific challenge is the design of a framework to create a strong connection between humans and machines in order to improve higher quality of geospatial products. In recent years a fast increment of activities has been observed on both: 1) citizen science and its expansion by the reuse of crowdsourcing data from other projects (e.g. OpenStreetMap). This happened in many scenarios including Earth Observation; and 2) information extraction from remotely sensed  data acquired by satellite-and airborne-based platforms. Remote sensing state of the art is wide from both methodological and application viewpoints. Both research fields are
acquiring more and more relevance in our society.
In this context the Ph.D. candidate will work on the design of novel methods for the fusion of remote sensing technology and citizen science for: i) novel products and services that may gain from the joint exploitation of the two complementary data sources; and ii) cross-validate remote sensing products by citizen science data or viceversa.
Candidates are preferred having a background in geoinformation analysis, image processing, computer science, physics and simila.
Contact: -