Grants and Contributions

May 2024 - This project seeks to support ALNAP’s 2024-2026 workplan and contribute to the promotion of lessons learned and the dissemination of best practices across the humanitarian sector.

To overcome this limitation, a surrogate evaluation function or a "surrogate model", is often used to replace the evaluation process and a learning approach is taken to estimate the parameters of the model. More specifically, the surrogate model is learned from a set of designs for which quality measurements have been obtained from the original evaluation process. This Project focuses on studying the effectiveness and robustness of such a learning approach and developing novel methods for this purpose.

P006358001: The project aims to improve learning for pre-primary, primary-aged and out-of-school girls and boys in the Cox's Bazar district of Bangladesh through gender equitable quality education.

These mirrors are manufactured on high precision single point diamond Computer Numerical Control (CNC) optical fabrication machines. This project will develop higher precision metal mirrors for these applications and will include development of new on machine metrology, analysis, and control capabilities to allow for the manufacture of both ultra high performance telescopes for science missions and high volume production of similar performance Low Earth Orbit (LEO) communication satellite telescopes.

The goal of this project is to create a self-driving lab (SDL)-based discovery platform for peptides by combining droplet microfluidics, cell-free protein expression, and machine learning (ML)-guided design. This project will use split optical proteins, which are composed of two parts, a peptide and larger protein fragment, as well as insulin, as model peptides and targets for high-throughput screening.

Analytical models for simulating and fitting experimental measurements will be developed, as well as machine learning algorithms for accelerating experimental throughput.

The Project combines advanced experimental techniques, density functional theory (DFT) and machine learning (ML) to develop and identify active, stable, and selective catalyst materials for CO2 reduction reaction (CO2RR).

The project will involve the generation of 3D data on the dimensional changes and distortions occurring during the BBAM process stages and apply the data in the development of machine learning models to enable fast adaptation of the initial design.