Work with us
ECLT offers a large variety of opportunities for research activities through calls for fellowship applications and grants. Please visit this section to stay up-to-date on open calls.
Call for Collaboration activities (150 hours) for CESA Center
Call for the assignment of collaboration activities (150 hours) for the support services for the activities of the CESA center.
Call for n. 1 Research Fellowship
Title: Game-theoretic models in interpretable machine learning
Tutor of the project: Prof. Marcello Pelillo
Duration: 12 months
Deadline: 14 May 2021, 12:00 pm CET
Call for applications to shortlist researchers authorised to submit collaborative proposals
A public call for proposals is open to select researchers holding a PhD degree authorised to submit collaborative project proposals to external funding programmes as Researcher in charge for the proposal. The following external funding programmes are admitted: European and International competitive calls and/or other types of funding that do not exclude the participation of research fellows as staff employed on the project and for which the candidate is eligible.
- European citizens and non-European citizens holding a PhD degree and a research and professional curriculum adequate to carry out the research activities;
- Documented research activities in the context of collaborative projects, at public and private entities with contracts, scholarships or assignments both in Italy and abroad.
Deadline: 06/12/2021 - 12.00 CET
ECLT has moved its laboratory at the new scientific campus [ITA] seat in Mestre (Venice).
The Laboratory has been realized with the support of the bank foundation "Fondazione di Venezia" and it is now at the disposal of students, researchers, professors of ECLT and Ca' Foscari University of Venice.
The "LivingTech Lab" is equipped with cutting-edge high-throughput screening equipment to allow the investigation of complex biological systems.
The initial equipment covers the following areas:
- Molecular biology: PCR, multi-label imaging system, 1D and 2D-electrophoresis system.
- Protein biochemistry: Low to middle scale expression and purification of recombinant proteins. Protein-protein and protein-DNA interaction. Enzyme characterization by polarised fluorescence, fast kinetic spectroscopy.
- High-throughput screening: 4-channels liquid handling robot equipped with a broad range of devices for microplate-based (from 96 to 1536-wells) biological and biochemical assays (Absorbance, glow and flash chemio- luminescence, bottom- top fluorescence, time-resolved fluorescence, polarised fluorescence for enzyme screening).
- Biological sample storage system: equipped with linear bar coding and sample tracking system.
- Analytical chemistry: High-throughput HPLC and FPLC coupled with UV-Vis spectrometer and fluorimeter.
Our goal is to study and develop computer algorithms that exhibit human-like cognitive abilities.
Our research focuses on the following areas:
- Machine learning
- Computer vision
- Pattern recognition
- Socio-ethical implications of AI
Our goal is to study the complexity of living systems using a holist approach bridging the borders between Physics, Chemistry, Biology and Medicine. To this aim, we combine computational and experimental tools to find molecular level solutions to a broad range of issues related to medicine, biotechnology, environment and energy.
Science of complexity
Any system in which a large number of elements interact, adapt and evolve to give rise to new meso- and macro-level structures can be described as complex. Learning about the organization and the dynamic of such systems raise challenging problems with respect to data - how to collect them, how to interpret them, how to use them to build models that can help to predict system dynamics. The Research Unit is devoted to design new methodologies for understanding the fundamental principles of complex systems at a variety of scales, from molecular biology to human societies. Research fields on which we are currently working include the following:
- Adaptive Experimental Design and Clinical Trials
- Evolutionary approaches for Multi-Objective Optimization
- Intelligent Systems for Data Analysis
- Machine Learning
- Bayesian Network and Graphs Analysis
- Predictive High Dimensional Models
- Computational medicine
Arts and complexity
For a long time, art and science were viewed as distant domains that were only loosely connected, but we’re now witnessing more interaction between the two. This has led to an increased awareness of how art and science are indeed two different but strongly coupled aspects of human creativity, both driving innovation as art influences science and technology, and as science and technology in turn inspire art. This Research Unit will start from these premises to face research challenges at the intersection of art, science, and technology.
The mission of this research unit is to promote the study of the human mind and its social and cultural complexities across major disciplines including neuropsychology, comparative cognitive neuroscience, affective neuroscience and computer science work towards an integrative view of sensory modalities and research methodologies.
G@V - GLOBAL AT VENICE: Research and Training for Global Challenge
G@V - GLOBAL_AT_VENICE: Research and Training for Global Challenges is a new 60-month fellowship project jointly funded by the European Union and Ca' Foscari University of Venice through the Marie Skłodowska-Curie Action COFUND.
The first call for the “Global at Venice - Research and Training for Global Challenges” Cofund Fellowship Programme is now open!
It will award 8 Fellowships each lasting 24 months.
The deadline to submit applications is 5pm (CET) June 30 2021.
The programme is supported by the University’s partners, including ECLT, non-academic networks of spin-offs, and small and medium enterprises, where Fellows will have the opportunity to complete secondments.
We kindly ask you to help us to promote and publicize this call as much as possible!
- Be in possession of a PhD degree awarded not later than 8 years prior to this call deadline.
- Have at least one major publication without their PhD supervisor (either accepted, in press or published) at the time of deadline.
- Have not resided or carried out their main activity in Italy for longer than 12 months during the 3 years prior to the call deadline, in compliance with the MSCA mobility rule.
Applicants are requested to submit their research proposal in one of the six interdisciplinary Research for Global Challenges Institutes (RGCI) that will support them with their individual research and training needs. Applicants are required to choose a potential supervisor whose role is to integrate the research within the Research for Global Challenge Institute.
The guidelines for applicants are available on the following website: Call and Useful info
European Learning and Intelligent Systems Excellence - ELISE
Project Title: European Learning and Intelligent Systems Excellence - ELISE
Start Date-End Date: 01/09/2020 - 31/08/2023
ECLT project leaders: Prof. Marcello Pelillo
Coordinator: AALTO KORKEAKOULUSAATIO SR (AALTO)
Funding Scheme: Call Identifier: H2020-ICT-2019-3
Topic: ICT-48-2020 - Towards a vibrant European network of AI excellence centres
Consortium: 23 institutions
ELISE aims to make Europe competitive in AI through a network of excellence. The best European researchers in machine learning and AI have worked together to attract talent, to foster research through collaboration, and to inspire and be inspired by industry and society. While ELISE starts from machine learning as the current most prominent method of AI, the network invites in all ways of reasoning, considering all types of data, applicable for almost all sectors of science and industry. While being aware of data safety and security, and while striving to explainable and trustworthy outcomes we aim to create a force to Europe.
ELISE will run a PhD student and a postdoc programme to attract and to educate world-class talents to Europe. It will operate a Fellows programme for groundbreaking research and high-profile workshops to develop AI methods applications further. Industry involvement is guaranteed by the many connections members of ELISE have with industry, on average one for every member and one start-up for every second member of ELISE.ELISE will demonstrate a fraction of their research in use cases to be implemented in AI4EU and the SME’s of Europe. Additional impact will be created to SME’s through open calls. The current practice of ELISE members of spin-off research in SME’s once a break-through is achieved will be stimulated through incubators.The current practice of participating in dissemination and debate that many members of ELISE are used to will be continued to develop a mature acceptance of AI throughout Europe for the benefit of all and in cooperation with all.
ELISE is built on 105 organisations in total, in which the 202 core contributors have actively indicated they will help build and profit from the networks of PhD-students and scholars.
ELISE includes 60 ERC grants of their active supporters. By their citation and other accepted scores of scientific quality, ELISE is the network that combines in Europe excellence in AI.
UNIVE-ECLT, as Linked-Third Party of the Consorzio Interuniversitario Nazionale per l’Informatica (CINI), will contribute to the realization of the following deliverables:
- D6.2: ELISE challenges accessible and showcased on the AI4EU platform (M36)
- D6.4: Booklet and media content on ELISE dissemination activities to the large public and industries (M24)
- D6.5: Booklet and media content on ELISE dissemination activities to the large public and industries (M36) as part of the WP6 "Dissemination and communication".
Project website: www.elise-ai.eu
Artificial intelligence assisted performance and anomaly detection and diagnostic
Project Title: Artificial intelligence assisted performance and anomaly detection and diagnostic
Start Date-End Date: 01/02/2020 - 30/04/2021
ECLT project leaders: Prof. Marcello Pelillo, Dr. Sebastiano Vascon
Funding Scheme: Tender ESA ITT AO/1-9845/19/UK/AB
The objective of the work is to define, design and validate a machine-learning-based method for the detection of Radio Frequency (RF) anomalies and the identification of the associated root causes, with the purpose of accelerating the RF equipment performance evaluation activity, supporting the domain experts in their analysis throughout the whole development, from design to qualification and Assembly, Integration and Test (AIT).
The study to be carried out aims at exploring the feasibility and demonstrating the use of Artificial Intelligence (AI) techniques to support RF experts in the anomaly detection and root-cause identification activity of antennas, or RF systems in general, performed in the development of the equipment from its design to the qualification and AIT phases, with the final purpose of reducing significantly the diagnosis time by a factor of 10÷100 (from about 2 weeks, typically required nowadays for antennas diagnostics, to about 1 day or less) while guaranteeing a minimum required performance in anomaly detection and root-cause identification.
The proposed solution has the objective to evaluate the status of the AUT or DUT, detecting the presence of potential anomalies and identifying the most likely root cause associated to it, in relation to possible flaws arisen during the development process, from design to qualification and AIT phases, including also defaults related to the measurement facility involved. Therefore, the project activity shall consist of a step-by-step process with iterations to design, implement and test a supervised machine-learning-based diagnostic tool capable of detecting RF systems anomalies and isolating the associated root causes, exploiting as much as possible the already existing data and information about antenna anomalies and EM diagnostic methods, in order to accelerate the whole diagnostic process.
Project Title: Drug combinations for rewriting trajectories of renal pathologies in type II diabetes - DC-ren
Start Date-End Date: 01/01/2020 - 31/12/2024
ECLT project leader: Prof. Irene Poli
Coordinator: Medizinische Universität Innsbruck
Funding Scheme: H2020-SC1-BHC-2018-2020, Topic SC1-BHC-02-2019
Diabetic Kidney Disease (DKD) is highly prevalent in type 2 diabetes, with major impact on patients and healthcare systems. The complex disorder, further modulated by cardiovascular comorbidities, presents as an accumulation of risk factors, which we treat with drug combinations. While the overall benefit of this approach is evident on a cohort level, individual patients show remarkable heterogeneity in drug response, and lack of guidance on personalized medication results in suboptimal control of the disorder. For resolving variability, we propose a new concept for personalization of drug combinations beyond the cohort-centric perspective. We improve patient stratification based on equivalence relations of clinical presentation, disease pathophysiology and drug combinations. The approach is derived from dynamical systems theory, aimed at reducing probabilistic assignment of patient-specific disease evolution and matching drug combinations. The availability of a large European repository holding DKD patients in routine care with diverse drug combinations, complemented by high-throughput screening for improving patient phenotyping, and molecular network modelling of pathology, embedded risk factor combinations and consequence of drug effect allows a systems representation of patient groups. Integrating clinical presentation and molecular architecture in a novel computational framework will establish a decision support software prototype. We will validate this tool for predicting optimized personalized drug combinations in a study using given clinical trial repositories. Demonstration will expand to other available drugs, which in combination with approved drugs promise benefit for groups of DKD patients. With a clear route toward uptake in the clinical setting, and generalization capacity of our approach to other complex disorders we foster next steps in personalization, anticipate major patient benefit, and see novel translation and business opportunities.
Team of research: Michele Braccini, Veronica Distefano, Maria Mannone, Claudio Silvestri, Debora Slanzi.
Budget for ECLT: 781,405.00 €
Project website: dc-ren.eu
Project Title: MEMories and EXperiences for inclusive digital storytelling - MEMEX
Start Date-End Date: 01/12/2019 - 30/11/2022
ECLT project leader: Prof. Marcello Pelillo
Coordinator: FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA
Funding Scheme: H2020-SC6-TRANSFORMATIONS-2018-2019-2020, Topic DT-TRANSFORMATIONS-11-2019
MEMEX promotes social cohesion through collaborative, heritage-related storytelling tools that provide access to tangible and intangible Cultural Heritage (CH) for communities at risk of exclusion. The project implements new actions for social science to: understand the NEEDS of such communities and co-design interfaces to suit their needs; DEVELOP the audience through participation strategies; while increasing the INCLUSION of communities. The fruition of this will be achieved through ground breaking ICT tools that provide a new paradigm for interaction with CH for all end user. MEMEX will create new assisted Augmented Reality (AR) experiences in the form of stories that intertwine the memories (expressed as videos, images or text) of the participating communities with the physical places / objects that surround them. To reach these objectives, MEMEX develop techniques to (semi-)automatically link images to their LOCATION and connect to a new opensource Knowledge Graph (KG). The KG will facilitate assisted storytelling by means of clustering that links consistently user data and CH assets in the KG. Finally, stories will be visualised onto smartphones by AR on top of the real world allowing to TELL an engaging narrative. MEMEX will be deployed and demonstrated on three pilots with unique communities. First, Barcelona’s Migrant Women, which raises the gender question around their inclusion in CH, giving them a voice to valorise their memories. Secondly, MEMEX will give access to the inhabitants of Paris’s XIX district, one of the largest immigrant settlements of Paris, to digital heritage repositories of over 1 million items to develop co-authored new history and memories connected to the artistic history of the district. Finally, first, second and third generation Portuguese migrants living in Lisbon will provide insights on how technology tools can enrich the lives of the participants.
Project website: memexproject.eu
Project Title: Reliable and Explainable Adversarial Machine Learning
Start Date-End Date: 29/08/2019-28/02/2023
ECLT project leader: Prof. Marcello Pelillo
Principal Investigator: Prof. Fabio Roli - Università degli Studi di Cagliari
Funding Scheme: Progetti di ricerca di Rilevante Interesse Nazionale - PRIN 2017
Machine-learning technologies have become pervasive, and even able to outperform humans on specific tasks. However, it has been shown that they suffer from hallucinations known as adversarial examples, i.e., imperceptible, adversarial perturbations to images, text and audio that fool these systems into perceiving things that are not there. This has severely questioned their suitability for mission-critical applications, including self-driving cars and autonomous vehicles. The defense strategies proposed to overcome this issue have been shown to be ineffective against more sophisticated attacks carefully crafted to bypass them, highlighting the challenging nature of this problem. In this project, we formulate three main challenges that demand for novel learning paradigms, able to take reliable and explainable decisions, to assess and mitigate the security risks associated to such potential misuses of machine learning. This project will pave the way towards the design of reliable and explainable machines that are also useful beyond adversarial settings. We will indeed develop tools and prototypes that can face the challenges posed not only by cybersecurity applications with a clear adversarial nature, but also by recent computer-vision and deep-learning technologies.
Project Title: A European AI On Demand Platform and Ecosystem (AI4EU)
Start Date-End Date: 01/01/2019-31/12/2021
ECLT project leader: Prof. Marcello Pelillo
Project Coordinator: THALES SERVICES SAS
Funding Scheme: Call Identifier H2020-ICT-2018-2020, Topic ICT-26-2018-2020
Consortium: 79 institutions
Artificial Intelligence is a disruptive technology of our times with expected impacts rivalling those of electricity or printing. Resources for innovation are currently dominated by giant tech companies in North America and China. To ensure European independence and leadership, we must invest wisely by bundling, connecting and opening our AI resources. AI4EU will efficiently build a comprehensive European AI-on-demand platform to lower barriers to innovation, to boost technology transfer and catalyse the growth of start-ups and SMEs in all sectors through Open calls and other actions. The platform will act as a broker, developer and one-stop shop providing and showcasing services, expertise, algorithms, software frameworks, development tools, components, modules, data, computing resources, prototyping functions and access to funding. Training will enable different user communities (engineers, civic leaders, etc.) to obtain skills and certifications. The AI4EU Platform will establish a world reference, built upon and interoperable with existing AI and data components (e.g. the Acumos open-source framework, QWT search engine..) and platforms. It will mobilize the whole European AI ecosystem and already unites 80 partners in 21 countries including researchers, innovators and related talents. Eight industry-driven AI pilots will demonstrate the value of the platform as an innovation tool. In order to enhance the platform, research on five key interconnected AI scientific areas will be carried out using platform technologies and results will be implemented. The pilots and research will showcase how AI4EU can stimulate scientific discovery and technological innovation. The AI4EU Ethical Observatory will be established to ensure the respect of human centred AI values and European regulations. Sustainability will be ensured via the creation of the AI4EU Foundation. The results will feed a new and comprehensive Strategic Research Innovation Agenda for Europe.
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