The objective of GRAPH-X is to develop a revolutionary hardware platform based on graphene-based Photonic Integrated Circuits (PICs) for use in the next generation of wireless communication technology. The project’s key component is a novel optoelectronic frequency mixer able to mix two optical wavelengths to access the D and WR4 frequency bands (from 110 to 260 GHz) in order to provide a solution for ultra-high speed and scalable sub-THz wireless links. The successful development of this technology has the potential to revolutionize the telecommunications industry, enabling faster and more reliable communication, which will facilitate the growth of emerging technologies such as 6G. In this context, the ability to generate those optical wavelengths with high-quality, tunability, and broad bandwidth capabilities in a compact form factor becomes a highly interesting opportunity that aligns well with the main goal of the project. The addition of ARC as a new partner, with wide expertise in developing miniaturized solutions for sub-THz generation, is expected to significantly enhance the GRAPH-X's objectives. We will develop a new miniaturized architecture on a photonic chip for the generation of sub-THz signals based on photonic techniques such as Optical Frequency Combs (OFC) and advanced filtering mechanisms that leverage on-chip laser devices. The system's interest lies in the fact that it combines the advantages of both electronic and photonic approaches for the generation of optical wavelengths that could readily synthesize sub-THz waves, giving rise to a new generation of systems with all the aforementioned characteristics. Furthermore, the architecture will be designed and implemented using PIC technology with the objective of offering a smaller size and lower power consumption while also addressing problems beyond the reach of traditional bulk, tabletop photonics.

Transparent ferroelectric oxide crystal with a strong 2nd-order optical nonlinearity are a paramount building block for electro-optical and nonlinear photonics bridging the electromagnetic spectrum from electrostatics and THz fields to optical frequencies. Currently, smart-cut ferroelectric thin films such are revolutionizing integrated photonics, overcoming the traditional limitations of bulk lithium niobate and silicon-on-insulator photonic integrated circuits such as low power efficiency and speed. Despite more than half a decade of tremendous scientific progress in the field, predominately using lithium niobate thin films, many technological and commercial hurdles have emerged compounding their adoption. The goal of ELLIPTIC is to overcome these limitations and to close the technology gaps that are still inherent to photonic integrated circuits based on ferroelectric thin films. LTOI will open new paradigm for nonlinear integrated photonics, based on its unique properties, such as a high optical damage threshold, reduced photorefractive effect, ultra-low optical and microwave loss and low birefringence. Moreover, LTOI leverages the existing micro-electronic manufacturing infrastructure due to its widespread adoption for 5G cellular signal filters. We will demonstrate the transformative potential of the LTOI platform for applications across various domains including optical and millimeter-wave communications, signal processing, metrology, frequency-comb generation, and quantum technologies, such as the transduction of quantum signals between superconducting microwave devices and optical fibers. The development of an process design kit (PDK) will democratize access to the technology for academia and the R&D communities.

The mission of this project is a methodological approach towards complementing the gaps in current HE courses and taking up high performance computing (HPC) knowledge for future science, technology, engineering and mathematics (STEM) professionals. HPC, often called super-computing, is a mandatory tool addressing problems that are too complex for desktop or laptop computers. For data analyzing, the size of the researched problem is limited by the RAM available in the computer and by the maximum object size. Therefore, data files with sizes up to a few GB can be analyzed. However, using HPC we can scale this limit up to several PB (1,000,000 GB) by using frameworks for distributed data storage and parallel processing. HPC enables researchers to use much bigger (therefore much more accurate) models (meaning many more equations and variables) to simulate the performance of new products, materials, medications etc. in a virtual environment.Our insight on the topic has revealed a regional wide requirement. Although in the smart strategy specialisation (S3) within the EU strategy, the topic of HPC adoption has been recognised, there is currently a lack of national programs. In recent years there is increased activity aiming at filling this gap – countries involved in this Erasmus+ call are establishing new HPC competence centres (EuroHPC program). Through them, basic educational programs are foreseen – HPC literacy programs for researchers, but a further systematical approach (bottom-up) is needed to efficiently raise the level of knowledge of HPC. Furthermore, the EU has made the decision to establish large “pre-exascale” and “exascale” trans-European centres that will meet the needs of scientific disciplines and be able to compete with similar centres in USA, China and Japan. In order to compete in this area of expertise, it is necessary to have experience in developing applications that exploit the high capacities of HPC. In recent years, there is an increased need of the industry to incorporate super-computing in their Research & Development and thus increase their own competitiveness.Unfortunately, current established courses in HE do not offer comprehensive support for HPC adoption. Therefore, researching and knowledge gathering through involvement of all project participants will raise the level of competences in STEM. Furthermore, success of the developed training will offer a starting point for future implementation of such a program into HE courses. Project partners included in the project are:- University of Ljubljana, Faculty for Mechanical Engineering, Slovenia,- IT4I, Czech national HPC center as part of VŠB University in Ostrava,- VSC (Vienna Scientific Cluster) Research Center of TU Wien, Austria,- CINECA, consortium of all Italian universities taking care of HPC and IT infrastructure.The project addresses the issue of raising the HPC knowledge by educating the educators and thus preparing them for future implementation. During the project duration, research in the field of HPC in Engineering and Data science is divided into four stages, each covered by an output:- O1; covering the topic of HPC in Engineering – focus on FEM (Finite Element Method),- O2; covering the topic of HPC in Data Science – focus on Parallelisation with MPI,- O3; covering the topic of HPC in Engineering – focus on CFD (Computational Fluid Dynamics),- O4; covering the topic of HPC in Data Science – focus on IOT and Big Data,- O5; combined collection of knowledge on the topic of HPC in Engineering and Data Science.Within each objective, output will include the creation of a knowledge base in form of lectures (core knowledge) and exercises/tutorials (hands-on approach) that form content, available through an e-learning portal. The e-learning portal enables better informing about the program, increases availability, tailoring of presented topics for specific area and offers better inclusion for enrolled participants. The relevance of the collected topics will be tested by introducing training events that will benefit both the verification of knowledge transfer to students and the enhancement of the educators by gaining experience. At the end of this program, the covered long term impact will present:1.) increase of knowledge and competences of educators (covered during every stage of project duration)2.) creation of 4 different training packages, available through the e-learning portal, that include both theoretical background and hands-on practical exercises (covered by O1-O4 output)3.) a well-organised accumulated knowledge covering individual topics of HPC in Engineering and Data science (covered by the last output O5) available through an e-learning portal and can be gradually introduced into current HE courses4.) training courses will enable transfer of knowledge to students already during the project duration (4 trainings, at each 7 participants from each project member, altogether 112 participants)

In this project, the participating organisations aimed to implement AlmaLaurea (AL) system (http://www.almalaurea.it/en) in Turkey. AL is a nonprofit interuniversity system designed and developed in Italy used by 65 member universities and the Italian Ministry of Education, University and Research. AL keeps track of students and plays a major role in matching university graduates and employers. Unlike commercial solutions through which employers and employees can match, AL system offers a more comprehensive solution both for the job matching process and for the enhancement of the education quality. AL system provides annual reports containing a detailed data analysis of graduates’ information (academic background certified by the university, satisfaction with the education received and with the university overall, geographical, social and economic background, work and internship experiences, future plans, and the sectors in which graduates work) to universities and participating organizations. It can be understand that the aim was to adapt the system according to the needs of the labor market and the higher education in Turkey. There were three participating universities from Turkey: Izmir University (which was closed due to statutory decree in July 2016), the coordinator, Ege University, and Sinop University.