Ο Αναπληρωτής Διευθυντής –με καθήκοντα Διευθύνοντα– του ΙΗΔΛ, Εμμανουήλ Στρατάκης, έχοντας υπόψη:

• Τη λήξη της θητείας του Ε.Σ. την 5.8.2026
• Το Άρθρο 15 του Ν. 4386/2016, που τροποποιεί το Άρθρο 17 του Ν. 4310/2014 που αφορά στην εκλογή και λειτουργία των Επιστημονικών Συμβουλίων των Ινστιτούτων
• Το υπ’ αριθμ. πρακτικού 332/21-5/8.6.2016 (ΠΑΡΑΡΤΗΜΑ Ι προκήρυξης) που αφορά στη Διαδικασία εκλογής μελών ΕΣΙ Ινστιτούτων και εκπροσώπου ΕΕΤΤΔΒΠ (πρώην ΕΤΔΠ)

Η Εφορευτική Επιτροπή (ΕΦΕ) αποτελείται από τα παρακάτω τακτικά μέλη:

• Αδάμ Αδικημενάκης ΕΛΕ  Γ’ 
• Ιωάννης Παραδεισανός, ΣμΔΕΠ (Επικ. Καθ.) 
• Αμαλία (Λία) Παπαδοπούλου, Διοικητικό Προσωπικό 

Με (ισάριθμα) αναπληρωματικά μέλη τα παρακάτω, αντίστοιχα:

• Εμμανουήλ Καργάκης, Διοικητικό Προσωπικό 
• Μαγδαληνή Κοκολάκη, Διοικητικό Προσωπικό 
• Ασημίνα (Μίνα) Παπαδάκη, Διοικητικό Προσωπικό 

Ο Αν. Διευθυντής του ΙΗΔΛ

Εμμανουήλ Στρατάκης

[Δυνάμει των Αποφάσεων του ΔΣ/ΙΤΕ υπ’ αριθμ. πρακτ. 503/41-6/16.5.2023 & 580/49-7/15.05.2026 για ορισμό του ως Αναπληρωτή Διευθυντή ΙΗΔΛ με καθήκοντα Διευθύνοντα]

Ηράκλειο, 19-5-2026

Για την πλήρη προκήρυξη των εκλογών Ε.Σ. μαζί με τα παραρτήματα σε μορφή PDF, ακολουθήστε τον σύνδεσμο "Brochure download" αριστερά

In the era of big data, machine learning (ML), a subfield of artificial intelligence, offers powerful tools widely used in science and industry due to their significantly lower computational cost compared to conventional methods. However, the accuracy of ML predictions relies heavily on identifying the right parameters (features) that enable effective learning from past data. Additionally, the quality and size of the dataset used to train the algorithm are critical for reliable predictions.

In traditional ML, feature extraction involves manually identifying and engineering key characteristics from raw data to enhance model performance. In contrast, deep learning (DL) automates this process through layered neural networks that learn relevant features directly from data, making it especially effective for complex tasks like image and speech recognition. This end-to-end approach allows DL models to process high-dimensional data with minimal preprocessing.

Here, we propose a deep learning framework for predicting gas adsorption properties of materials, using image recognition algorithms. We use images of the Potential Energy Surface (PES) and the Point Clouds (PC) of MOFs as descriptors. PES uniquely combines the material's classical structural characteristics with its quantum electronic structure, encapsulating the material's information in both classical and quantum worlds - a fusion of chemistry and physics. On the other hand, PCs is a minimal representation of the material’s 3D structure, eliminating the need for hand-crafted features.

Both methodologies were validated across different materials, gases, thermodynamic conditions and datasets, demonstrating broad applicability for diverse materials and properties. Moreover, the importance of physical grounded descriptors like PES was verified.

*Joined with FORTH / ICS

Optical wireless communications (OWC), and in particular LiFi, have emerged as a compelling complement to radio-frequency technologies, offering vast unlicensed spectrum, inherent security, and the potential for ultra-high data rates. As we move towards 6G and beyond, the role of light is evolving from a communication medium to a key enabler of sensing, intelligence, and environmental awareness. This talk provides an introduction to OWC, starting from the fundamental principles of light-based communication and the evolution of LiFi systems. It then explores recent advances that extend these systems beyond data transmission, highlighting how optical signals can be leveraged for high-precision sensing, localisation, and environmental mapping. The talk will introduce the concept of intelligent optical environments, where communication and sensing are tightly integrated within programmable and adaptive infrastructures. Such environments have the potential to enhance network resilience, strengthen physical-layer security, and enable new context-aware services. Finally, the talk outlines key research challenges and future directions in the design of scalable, robust, and intelligent OWC systems, positioning optical wireless technologies as a cornerstone of next-generation wireless networks.

Optical fiber sensors offer a unique combination of compactness, immunity to electromagnetic interference, high sensitivity, and the ability to operate in demanding environments. Their multiplexing capability and compatibility with sensing, transducing materials make them particularly attractive for real-time monitoring across a wide range of applications.

The presentation will outline the development of application-driven optical fiber sensors, across multiple domains, including precision agriculture, industrial monitoring, environmental assessment and biomedicine. Key optical fiber sensing platforms, including Fiber Bragg Gratings, Long Period Gratings, Fabry–Pérot cavities, and No-Core Fibers, will be introduced as versatile building blocks enabling sensing of physical, chemical, and biological parameters.