With the proposed research project, I aim to revolutionize optical metasurface design leveraging surrogate modelling techniques (OBJ1) towards the development of 3D-printed, achromatic, high numerical aperture (NA) metalens-enhanced fibres, called metafibres (OBJ2). The proposed methodology consists of three interconnected phases: firstly (WP1), constructing a supercell meta-atom library consisting of three-dimensional metaatoms through iterative optimization. The simulated designs will be 3D-printed and the simulation model updated based on the results of the experimental characterization, ensuring precise and realistic control of meta-atom responses. Secondly (WP2), a Gaussian Process Regression (GPR) will be implemented as a versatile surrogate model using the established supercell library. This machine learning technique enables the efficient prediction of complex optical meta supercell responses, facilitating rapid prototyping and customization with reduced computational overhead. Notably, GPR models allow to consider fabrication tolerances straight-forward in combination with data augmentation techniques. Lastly (WP3), I will exploit the GPR model for the design of high-performing achromatic metafibres, utilizing 3D-printing technology to directly print the metalenses on the tip of optical fibres. I have chosen a top research centre, Leibniz Institute of Photonic Technology/ Jena, Germany, to develop my MSCA project under supervision of a world-leading expert in optical fiber photonics. Using my knowledge in engineering, photonics and numerical simulation tools, this MSCA project will pave the way for metafibres promising versatile and broadband optical solutions with the potential to transform fields like microscopy, telecommunications and medical imaging. This research aligns with sustainability goals by minimizing material waste and energy consumption in the fabrication process, marking a significant advancement in metasurface and optical system design.

Severe neuronal disorders such as dementia affect more than 1 billion people globally and account for economic burden far exceeding that of cancer. Our only chance of treating dementia is improving our understanding of how brains function at diverse levels of complexity. This will only be possible through better technologies, which already reflects in synchronised actions worldwide, such as the US Brain Initiative and EU Human Brain project. The origin ERC project LIFEGATE has developed minimally invasive (hair-thin) endoscopes for advanced applications in neuroscience, and brought about a solid route to their commercial outreach. The instruments rely on state-of-the-art digital holographic methods combined with high-performance computing. They offer uniquely detailed observations in unprecedented depths of living, yet anaesthetized, animal models, making them particularly suitable to studies of connectivity of neuronal cells inside their natural environment. This proposal, entitled WOKEGATE, represents a powerful extension of this concept, enabling imaging-informed, single-cell specific activity monitoring and control of deep neuronal circuits in fully awake and behaving animals. The prospect is centred around a novel M3CF fibre probes which emerged within the origin ERC project LIFEGATE, outside its proposed research agenda. The project aims for (i) necessary technological amendments to incorporate this opportunity into the holographic endoscope geometries, (ii) a direct verification of this prospect in in-vivo studies through the network of existing research partners and (iii) paving the way for commercial translation of such empowered scientific instrumentation. Successful acquisition and execution of the WOKEGATE will enable future perspectives of this promising technology through new opportunities such as the EIC Accelerator grant.

The objective of this project is the development of high quality poly silicon (poly-Si) thin films on glass applying liquid-phase crystallization by line focus laser irradiation. Introducing an adequate interface layer between the glass and the silicon film and applying laser crystallization by scanning over thin amorphous or nano-crystalline silicon thin films on glass has been shown to yield high-quality poly-Si films for solar cells. These films on glass present also high potential for other electronic devices like e.g. flat panel displays. In photovoltaic (PV) application this technology could result in significant silicon material savings and therefore cost reduction of PV modules in the near future. In the electronic industry it could give new possibilities to fabricate highly integrated electronic circuits on large area. In the frame of this project the investigation and optimization of the laser crystallization process and the design of the interface layer either for low cost soda-lime float glass or ultra-thin high temperature glass will be a focus. Solar cells and mini modules will be fabricated with the aim to develop on the one hand a process technology for large area monolithic integrated poly-Si thin film modules and on the other hand low cost wafer equivalents for back contacted solar cells which on the long-term can achieve efficiencies of multi-crystalline wafer cells. For characterization and analysis of the electronic and optical properties of the glass/poly-Si substrates and solar cells injection level dependent photoluminescence and spectral response measurements will be further developed and implemented.

In most European countries, the diagnosis of cancer is achieved by examination of haematoxylin-eosin (HE) staining by an experienced pathologist. Nevertheless, several other diagnostic approaches exist (e.g., immunohistochemical staining) which are not applied routinely for all cases due to their technical complexity, duration, and cost. Therefore, an important unmet medical need for fast, non-invasive, and label-free immunohistochemical staining based on molecular imaging without laborious sample treatment exists. This demanding challenge will be tackled in STAIN-IT using a non-invasive label-free measurement technique called multimodal imaging (e.g., the combination of coherent anti-Stokes Raman scattering, second harmonic generation, and two-photon-excited fluorescence). The multimodal images will be analysed using deep learning approaches, such as convolution neural networks (CNNs). These CNNs are utilized to mimic immunohistochemical stainings. CNNs are neural networks that learn the feature representation of the data, which is optimally suited to model a specific immunohistochemical staining. In STAIN-IT, the staining models will be developed along with the methods to quantitatively understand the nonlinear behaviour of the CNNs. With the envisioned approximation approaches for CNNs, these models no longer act as ‘black box’ systems, and a quantification of tissue changes associated with the staining models can be achieved. For the very first time, STAIN-IT will develop a label-free, non-invasive, labour-inexpensive, and fast computational immunohistochemical staining, which can be easily implemented into clinical routine yielding increased diagnostic reliability and a better understanding of disease pathogenesis. A fast test of the antigen KI-67 in an intraoperative frozen section consultation situation or the use of Collagen IV as a quality control marker of tissue-engineered medicines are some of the exciting application possibilities of such staining model.

The current biosensing marker for healthcare and environmental applications is dominated by laboratory instrumentation that is bulky, time consuming, and expensive and requires well-equipped laboratories and skilled personnel. Many essential applications, such as disease diagnostics (e.g. hepatitis B, tuberculosis, HIV) and water quality monitoring (e.g. pathogens, heavy metals, toxins), are often neglected due to economic reasons, causing millions of deaths globally. The most significant challenges facing biosensors are their fabrication and instrumentation costs, insensitivity, and low-throughput detection. One technique capable of tackling these challenges is localised surface plasmon resonance which has demonstrated the potential to deliver cost-effective hand-held point-of-care devices with rapid, multi-analyte detection. To date, this potential has been limited by the available techniques for fabricating feasible nanoscale sensors. I have chosen a world-recognized research centre, the Leibniz Institute of Photonic Technology in Germany, as my host institution to develop my MSCA under the supervision of an excellent mentor in nanobiophotonics, where I will be trained in advanced techniques in nanophotonics. The acquired skills in combination with my own knowledge will perfectly match for developing a novel nanofabrication technique to deliver affordable and versatile sensors based on a 2D array of well-ordered and dense gold nanoparticles - a unique blend of interdisciplinary methods with the potential to rival lithography techniques but at a fraction of their costs. Precise control over nanostructured dimensions will allow me to tune the sensor for biosensing applications. Microarray biofunctionalisation of the sensor will demonstrate multiplexed detection of pathogens (DNA), disease biomarkers (proteins), and heavy metals and will pave the way for improved healthcare and environmental screening, where test availability, speed, and cost play a decisive role.