Printable versionDetails about funded positions - 41st Cycle - Curriculum 6
6A - Model-based system-software engineering and formal methods for space systems
Funding institution: Fondazione Bruno Kessler FBK
Doctoral site: Fondazione Bruno Kessler FBK
Contact: Marco Bozzano [bozzano@fbk.eu]
Funds: Own Funds
Mobility abroad: compulsory, minimum 6 months
Periods in companies/research centres/public administrations: optional
Space systems have reached an unprecedented degree of complexity. The design process has to guarantee not only the functional correctness of the implemented system, but also its dependability and resilience with respect to run-time faults. Hence, the design process must characterize the likelihood of faults, mitigate possible failures, and assess the effectiveness of the adopted mitigation measures.
Formal methods have been increasingly used over the last decades to deal with the shortcomings of designing complex systems, in different domains. Formal methods are based on the adoption of a formal, mathematical model of the system, shared between all actors involved in the system design, and on a tool-supported methodology to aid all the steps of the design, from the definition of the architecture down to the final implementation in HW and SW.
The objective of this study is to advance the state-of-the-art in space system design using formal methods. In particular, it will investigate new techniques for model-based system and software engineering, to support the design, mission preparation and operations of space systems. The potential research directions include fault detection, isolation, and recovery (FDIR) for satellites and space exploration systems; system-level diagnosability, diagnosis and root-cause analysis; anomaly detection and FDIR based on machine learning techniques. Topics to be investigated include techniques for contract-based design and contract-based safety assessment, the analysis of the timing aspects of fault propagation, the characterization of transient and sporadic faults, the analysis of the effectiveness of fault mitigation measures in presence of complex fault patterns, the use of machine learning techniques for anomaly detection and fault classification and their integration with FDIR.
Intellectual Property Notice for PhD candidates under the UniTrento-FBK Agreement. Please read the following information carefully before submitting your application.
Intellectual Property of Research Results.The intellectual property rights of research results generated by PhD students under scholarships within the UniTrento-FBK Agreement shall belong to FBK.
Transfer of Intellectual Property Rights. FBK will establish agreements with PhD students regarding the transfer of intellectual property rights related to their research results.
Collaboration with UniTrento. If UniTrento academic staff contribute to research results obtained through PhD scholarships funded by FBK, the determination of IP shares will be defined through separate written agreements based on each party’s contribution. PhD students are required to collaborate with UniTrento in all necessary activities related to the joint management of IP.
6B - Development of millimeter/sub-millimeter-wave components for Space payloads through Advanced Manufacturing
Funding institution: Consiglio Nazionale delle Ricerche - CNR
Doctoral site: CNR-IEIIT (Torino) or CNR-STIIMA (Milano)
Contact: Oscar Peverini [oscarantonio.peverini@cnr.it]; Irene Fassi [Irene.Fassi@stiima.cnr.it]
Funds: Own Funds
Mobility abroad: compulsory, minimum 6 months
Periods in companies/research centres/public administrations: optional
Within this Ph.D., the candidate will investigate the manufacturability of radiofrequency breadboards for Space applications exploiting advanced manufacturing technologies, both subtractive, such as micro-EDM, and additive, such as metal extrusion-based additive manufacturing and precision 3D printing of functionally graded materials. Indeed, next-generation payloads for SatCom (in GEO and LEO orbits), Earth Observation, and Space Science will require high-volume production of high-performance RF instrumentation operating from the Ka/Q/V bands (30-50 GHz) up to sub-millimeter frequencies (200-600 GHz). These applications require manufacturing technologies able to fabricate high precision components with complex geometry and accurate micro-features, with high throughput, zero waste and sustainable footprint. In this context, precision manufacturing technologies play an enabling role for the development of innovative payloads in terms of performance and compatibility with platforms. This multi-disciplinary study will be carried out at the two institutes STIIMA (https://www.stiima.cnr.it) and IEIIT (https://www.ieiit.cnr.it) of the CNR in synergy with the research activities that the CNR carries out within European Space Agency programmes.
6C - Power processing and control for CubeSat scale air-breathing electric propulsion (CUP J53C23001840006)
Funding institution: Sant'Anna School of Advanced Studies Pisa
Doctoral site: Sant'Anna School of Advanced Studies Pisa
Contact: Tommaso Andreussi [Tommaso.Andreussi@santannapisa.it]
Funds: ERC BREATHE
Mobility abroad: compulsory, minimum 6 months. Justus Liebig University Giessen (TBC)
Periods in companies/research centres/public administrations: optional
The growing demand for satellite-based services is driving spacecraft miniaturization and the deployment of large constellations. To reduce environmental impact, future missions are moving toward very-low Earth orbits (VLEO), which offer better performance, lower radiation exposure, and natural debris mitigation. Sustained operations in VLEO require electric propulsion, with air-breathing electric propulsion (ABEP) being a promising solution. This PhD project focuses on power processing and control for CubeSat-scale ABEP systems. It aims to define the constraints and requirements of propulsion power electronics, study integration strategies within CubeSat platforms, and develop optimal control approaches for orbit maintenance under power limitations and varying atmospheric conditions. A key objective is the development of a power processing and control prototype tailored to ABEP in small satellite systems.
Grant Agreement number: 101088694
CUP n. J53C23001840006
Project name: Building a space Revolution: Electric Air-breathing Technology for Highatmosphere Exploration
Project acronym: BREATHE ERC-2022-COG
Type of action: HORIZON ERC Grants Granting authority: European Research Council Executive Agency
6D - Advanced models for the design and characterization of deployable antennas
Funding institution: Politecnico of Turin
Doctoral site: Politecnico of Turin
Contact: Erasmo Carrera [erasmo.carrera@polito.it]
Funds: Own Funds
Mobility abroad: compulsory, minimum 6 months [California Institute of Technology]
Periods in companies/research centres/public administrations: optional
The design and characterization of large deployable structures, such as antennas, star shields or solar sails, pose significant challenges that require advanced modeling techniques. This research focuses on developing advanced models to enhance the design and characterization process of those structures.
One aspect of this research involves utilizing high order finite element modeling. These advanced numerical models will enable accurate prediction of the mechanical behavior, structural integrity, and overall performance of deployable antenna systems for example. By incorporating multi-scale mechanics, the interactions between various structural components at different length scales will be comprehensively understood and analyzed.
Furthermore, the research will explore the use of advanced materials, such as carbon fiber-reinforced polymer (CFRP) composites or soft hyperelastic materials, to optimize the performance, weight, and reliability of deployable antennas. The design considerations and manufacturing techniques specific to these materials will be studied and incorporated into the modeling process. Particular attention will be focused on the mechanics and multi-field analysis of multi-functional membranes, TRAC booms, collapsible longeron etc.
In addition to finite element simulations, multi-body analyses will be employed to investigate the dynamic behavior and deployment mechanisms of the antennas. These simulations will analyze the complex interactions between the deployable antenna structure, deployment mechanisms, and external forces, leading to improved designs and enhanced operational efficiency.
To validate and refine the developed models, experimental testing will be conducted. This hands-on approach involves constructing scaled prototypes, performing structural tests, and measuring critical performance parameters. The experimental results will be correlated with the numerical models to ensure accuracy and reliability.
This PhD position offers a stimulating research environment, access to state-of-the-art facilities, and collaboration opportunities with leading experts in the field of aerospace engineering. Prospective candidates with a strong background in aerospace engineering, mechanical engineering, or a related discipline are encouraged to apply. Proficiency in numerical modeling, finite element analysis, and programming languages will be advantageous.
6E - Computational modelling of damage and aging of structures and materials exposed to harsh extraterrestrial environments
Funding institution: University of Palermo
Doctoral site: University of Palermo
Contact: Ivano Benedetti [ivano.benedetti@unipa.it]
Funds: Own Funds
Mobility abroad: compulsory, minimum 6 months
Periods in companies/research centres/public administrations: optional
Space missions like NASA’s Artemis and ESA’s ExoMars are pushing material science limits, exposing structures to extreme space environments, e.g. high and cryogenic temperatures, rapid thermal cycles, radiation, and particulate abrasion—causing thermal fatigue, aging, cracking, and wear that threaten long-term integrity. Mission success requires tools to simulate these coupled degradation modes.
The project will develop a multi-scale/physics high-performance computational framework for heterogeneous materials able to capture material and structural performance degradation under such complex loads. Starting from a tool already available in the research team, the framework will include: (i) extended multi-physics coupling, e.g. thermochemical and radiation effects, thermal cycles, etc.; (ii) extended constitutive modelling capabilities. It will support material selection, lifetime prediction, and mitigation strategies, including self-healing systems, for safer deep-space missions.
6F - Deep Learning techniques for inverse problem in imaging
Funding institution: University of Brescia & Be2net s.r.l.
Doctoral site: University of Brescia
Contact: Davide Pagano [davide.pagano@unibs.it]
Funds: Own Funds
Mobility abroad: compulsory, minimum 6 months [Laboratoire Hubert Curien, 18 RUE Pr Benoît Lauras 42000 SAINT-ETIENNE FRANCE]
Periods in companies/research centres/public administrations: optional
Inverse problems deal with the reconstruction of an unknown signal, image, or multi-dimensional volume from a set of observations, which are generally the result of a non-invertible forward process. A wide range of scenarios fall within this definition, such as deconvolution, image deblurring, inpainting, and so on. As inverse problems are usually ill-posed, in general, it is not possible to find a unique solution that describe the observation, unless some prior knowledge about the data is available. Traditional approaches, based on the minimazion of a cost function and a regularizer taking into account possible prior knowledge on data, are found to underperform with respect to recent deep learning techniques. The goal of the project is to explore the potentiality of modern deep learning techniques in selected imaging cases, such as cosmic-rays applications, which are relevant to science and industry.