Robotics can offer numerous opportunities to a wide range of market domains in a developing country like India, such as manufacturing, agriculture, transport and logistics, space exploration, etc. However, the use of robotics in India has been mainly challenged by the high cost of adoption, lack of accessibility, and the lack of skilled talent in robotics technology. The current project, IRAS-HUB, addresses the lack of skilled talent in robotics technology in India by the establishment of three hubs in robotics and autonomous systems (RAS).Project IRAS-HUB will achieve the following results:1. 3 RAS hubs set up at and equipped with prototyping equipment and robotics software in three Indian HEIs.2. 22 faculties from Indian HEIs will be trained in RAS by reputable researchers and experts from EU HEIs.3. 8 courses in robotics at Indian HEIs will be developed and/or modernized. 4. 1 standardized training program will be developed for the continued learning of working professionals in RAS.5. 3 industry-driven pilot projects in RAS will be developed in India, one in each Indian HEI.6. 220 senior UG and PG students will be taught through the developed and modernized semester-long courses.7. 60 working professionals in RAS from other Indian HEIs and robotics industries will be formally trained in RAS through the developed training program.Project IRAS-HUB envisions to achieve the following impact:1. Development of highly knowledgeable and skilled human resources in RAS in India.2. Promotion of knowledge generation in robotics technology through basic and applied research. 3. Development of robotics technology for problem-solving in diverse sectors of India such as agriculture, transportation, etc.4. Promotion of competencies, capacity building, and training to nurture innovation and start-ups and aspiring entrepreneurs in robotics.5. Internationalization and modernization of Indian HEIs by connecting Indian HEI’s with global efforts in robotics education.

The long term vision in BAYFLEX is to create a radically new technology that uses low cost, green organic electronics for probabilistic computing in order to allow continuous and private monitoring of bio-signals on flexible substrates. The vision of flexible green AI sensors with on chip classification extends well beyond biomedical devices and the democratization of health care, with the possibility to transform sensor data at the edge of large networks. To achieve our goal, BAYFLEX will demonstrate a patch using active physiological sensors based on organic materials that interface with the soft human body and that also includes classification circuits (~ 100 transistors) fabricated using Thin Organic Large Area Electronics (TOLAE) processes. These circuits use spiking neurons realized in Organic Thin Film Transistors (OTFTs) to transform the non-stationary electrical signals from the sensors into stochastic bit streams. Bayesian inference is then used to classify the data using circuits of cascaded Muller C-elements. Taking advantage of the unique properties of organic electrochemical transistors (OECTs), low transistor count dynamic Muller C-elements are targeted. The patch will be tested on a simple task using healthy humans. The project brings together an interdisciplinary consortium with expertise in modeling emerging devices, biologically inspired circuit design, experts in machine learning involving electrophysiological data (including an SME) and teams with expertise in OTFT and OECT fabrication. BAYFLEX targets dissemination to a variety of publics including: scientists via publications in (open access) high impact journals and conferences; industrials and end-users through an industrial advisory board, a workshop and demonstrations at targeted conferences; the general public with the creation of a transferable workshop for non-scientific communities and training the next generation of experts through specialized schools and workshops.

Ancient irrigation systems are probably the first human large-scale landscape modifications. The analysis of the ancient irrigation systems that gave rise to and sustained the first urban civilisations goes well-beyond the study of ancient irrigation to touch topics such as climate change, sustainability, population dynamics, and ancient economy, all at the core of urbanisation as a deeply human phenomenon, perhaps the most important change in the history of humanity. For many decades ancient water management research in archaeology was mainly focused on the Near East and surrounding regions with similar hydroclimatic conditions. The political situation in that region and, as a consequence, the lack of new data nearly stopped further scientific activities. The relevance of this research topic, however, did not decrease and many methods for the study of ancient irrigation and water management using remote sensing haven been developed during the last years. However, most previous approaches are region-focused (lacking applicability to other environments) and have mostly detected isolated channels, which complicates the study and understanding of the network that constituted ancient irrigation systems. UnderTheSands will employ a combination of novel remote sensing techniques (including Multi-Scale Relief Model, Seasonal Multitemporal Vegetation Indices, hybrid machine-deep learning algorithms, archaeomorphology, spatial correlation indices, and historical analyses) and sources (including multispectral imaging, synthetic aperture radar, and TanDEM-X) to produce a workflow for the detection and analysis of ancient irrigation networks in diverse environments. These methods and the training provided during their implementation by a team of leading international researchers will enhance the candidate's profile with cutting-edge techniques that will situate him at the forefront of research in ancient irrigation and boost his career.

The collapse of Bronze Age societies in the Aegean led to a period known as the ‘Greek Dark Ages’ (Early Iron Age, EIA) that involved major socio-economic disruptions and transformations. The causes of these changes and their impact on the agricultural economy of these societies have been matters of a long-lasting debate. The nature of agricultural management and production in this period is largely speculative due to limited bioarchaeological data and the inherent limitations involved in the analysis of available evidence. Owing to the lack of knowledge regarding the role of agriculture in these processes of socio-economic turmoil, a new approach to the debate is needed. DarkSeeds aims to re-examine the role of agriculture to provide an evidence-based understanding and a new explanatory model of the economic changes during the Late Bronze Age (LBA) – EIA, through a novel combination of traditional and newly developed methods and emerging technologies. In particular, it will employ archaeobotanical material from six sites across the Aegean to capture spatial variability and will: (1) use standard archaeobotanical approaches, including synthesis of existing data; and (2) apply and refine a new methodology, pioneered by the supervisory team, that combines 3D photogrammetry, Machine Learning-aided Geometric Morphometrics (GMM) and targeted stable isotope analysis, and uses directly the 3D shape of cereal seeds to infer agricultural practices. This combination of traditional with advanced and newly developed scientific techniques has the potential to step-change the investigation of complex issues of agricultural change and provide new, in-depth understandings of the very processes that underpinned the LBA-EIA societies. DarkSeeds will build upon the Experienced Researcher’s expertise to equip her with an excellent toolkit, through hands-on training, that can place her at the forefront of cutting-edge research and establish her at the top of her field.