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GRADUATE STUDIES
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MSc in Optics, Image, Vision, Multimedia (OIVM)
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iPSRS - Intelligent Photonics for Security, Reliability, Sustainability and Safety
- PSRS - Partner universities
- RADMEP - Radiation and its Effects on MicroElectronics and Photonics Technologies
- COSI - Computational Colour and Spectral Imaging
- IMLEX - Imaging & Light in Extended Reality
- AIMA - Advanced Imaging & Material Appearance
- PE - Photonics Engineering
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iPSRS - Intelligent Photonics for Security, Reliability, Sustainability and Safety
- MSc in Computer Science
- MSc in Health Engineering
- Engineering schools' research tracks
- Doctoral studies
- Training through research
- Opportunities
- Admission and aid
- OPTICA student chapter
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RESEARCH & INNOVATION
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SCIENTIFIC EVENTS
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The SLEIGHT Science Events
- SSE #13 - SLEIGHT in 2025
- SSE #12 - Imaging in Manutech-SLEIGHT
- SSE #11 - SLEIGHT in 2024
- SSE #10 - Sustainable Surface Engineering
- SSE #09 - SLEIGHT in 2023
- SSE #08 - Photonics for Health
- SSE #07 - SLEIGHT in 2022
- SSE #06 - Machine Learning
- SSE #05 - SLEIGHT in 2021
- SSE #03 - SLEIGHT in 2020
- SSE #02 - Material Appearance
- SSE #01 - Topics and stakeholders
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SCIENTIFIC AXIS 2: Extract full information and meaning from surface imaging through an integrated chain of skills
The second scientific axis will provide, over a short term, scientific outcomes such as novel imaging methodologies encompassing the full image chain, from the physical basis to mechanical insights and data intelligence, developing, among others, alternate imaging systems and associated reconstruction methods– e.g. for microscopy and astronomy. It will impact medical imaging by developing safer, faster, and more elaborate diagnosis tools, and the industry by offering improved possible in-situ control of fabrication processes.
The main goal of the 2nd scientific axis is to bring coherence and create a continuous chain ranging from the optical design to the modelling and the characterization of images, the geometrical analysis up to recognition and detection tasks by artificial intelligence-based methods, while maintaining the excellence of each of these items. The scientific locks to overcome to achieve this target will be addressed in the medium term (4years):
- Developing dedicated optical design for applications requiring in situ imaging (imaging in harsh environment, in situ diagnostic of biological tissues and thermo-mechanical properties).
- Defining quantitative imaging concepts with in-situ capability.
- Modelling visual rendering to gain predictive ability: application to the specification of gonio-chromatic reference samples and development of advanced gonio-spectrometric instruments for control in industry.
- Developing new invariant colour descriptors for polarized and gonio-chromatic spectral images.
- Designing new robust algorithms for machine learning and data analysis of multiview, heterogeneous images exhibiting a complex structure.
- © MSebban - LabHCAxe2_illus1_MSebban
- © MSebban - LabHCAxe2_illus2_MSebban
RESEARCH PROJECTS
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Unveiling and Incorporating Knowledge in Physics-Guided Machine Learning Models
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TRansfer lEarning for Frugal and Accurate modeling of SURface Functionalization prediction –application to multicomponent alloys
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Time-Resolved Inference of friction Using Machine-learning informed by a Physical model
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Diffusion Of Nanoparticles On Surfaces
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Quantitative identification of physical characteristics of solid interfaces correlated with the tribological properties
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Predicting the Reflectance and Transmittance of Translucent Dental Resins
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MAchine Learning for high definition BOne digital Twin
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Intravascular manometry by vector Doppler ultrasound
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Physics-Guided Machine Learning
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Quantitative analysis of biomarkers by spectrometry
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Explainable deep models in cell imaging: application to the analysis of structural changes in human cells for diagnostic purposes
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Physics-Informed MAchine LEArning: From Extraction to Transfer of Knowledge in Surface Engineering
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Light and mixed reality for visual assistance for the elderly
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Quantitative in situ imaging and machine learning of cell/matrix mechanical interactions for cell biology
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Representation and Machine Learning with Missing Data