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A new method significantly advances 3D imaging of reflective surfaces. The approach integrates techniques known from high-precision optical 3D metrology and computer vision, and could benefit applications ranging from industrial inspection and medical imaging to virtual reality and cultural heritage preservation.
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Most haptic devices only deliver feedback as simple vibrations. New device applies dynamic forces in any direction to simulate a more realistic sense of touch. Small, lightweight device can enhance virtual reality, help individuals with visual impairments, provide tactile feedback for remote health visits and more.
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Research theme area:
Organic carbon dynamics and organo-mineral interaction characterization in mangrove soils along the Brazilian coast
Abstract:
The candidate will be based in Fortaleza (Ceará, NE-Brazil), under the supervision of Prof. Gabriel Nuto Nóbrega from the Department of Soil Sciences (UFC), and will collaborate with researchers from the BlueShore project, coordinated by Prof. Tiago Osório Ferreira (ESALQ/USP), part of the FAPESP-Shell Research Centre for Gas Innovation at POLI-USP, University of São Paulo. A summary of the program and the projects can be found on the RCGI website (https://sites.usp.br/rcgi/).
Description:
The applicant will contribute in line with the main objectives of the project:
1. Quantify carbon stocks (in the soil and aboveground) and greenhouse gas (GHG) fluxes (CO2, CH4, and N2O) of mangrove forests under different conditions (pristine, degraded, replanted).
2. Understand the mechanisms of carbon storage and stabilization in mangrove soils.
3. Calculate carbon sequestration rates in reforested and
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Research theme area:
Postdoctoral Opportunity in Hydrogen Production Reactors, Advanced Image Analysis, Experimental Multiphase Flow, and Particle Image Velocimetry.
Abstract:
The candidate will collaborate with researchers from the project 56 FAPESP Research Centre for Greenhouse Gas Innovation of POLI-USP at the University of São Paulo. Summary of the program and projects can be found at the RCGI website (https://sites.usp.br/rcgi/).
The selected candidate will employ machine learning to perform flow analysis with a focus on noise removal, construction of neural networks and unsupervised methods. The candidate must work with experimental and numerical source data.
Description:
The applicant will contribute in line with the main objectives of the project:
1. Use and/or develop image analysis approaches to study multiphase flows in hydrogen production through electrochemical reactors.
2. Work with flows obtained experimentally and synthetic image analysis.
3. Understand experimental and numerical methods related to fluid mechanics.
4. Collaborate closely with a multidisciplinary team of
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