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PhD Student - Physical modeling via deep neural networks

Job Summary

We are currently accepting applications for the above mentioned position. This is a unique opportunity for highly motivated students, recently graduated from the university in Physics or related fields, to join one of DIPC’s high-profile research teams.

A PhD student is being sought to carry out a project in Deep Neural Networks (DNNs), based in the Dept. of Polymers and Advanced Materials: Physics, Chemistry and Technology, Faculty of Chemistry, University of the Basque Country, San Sebastian, Spain (UPV/EHU) in active collaboration with Dept. of Mechanical Engineering, Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, China (SEU).

Job Description

The project is based on a recent finding by the two groups. For physical systems exhibiting a strong spatial dependence (usually as a function of a few relevant experimental parameters) and when the number of images that display the spatial dependence is very reduced (due to excessive cost/time-consumption/legal-constrains/etc), one can directly use the spatial dependence to segment the images into physically meaningful regions, whereafter every pixel from every image will belong to one region (or class/label), thus resulting in a truly large pixel-based dataset with millions of classified pixels, even when obtained from a few images. The key idea is to use this simple piece of information (the region/class/label) as the output for two equally simple inputs: the experimental conditions and the pixel’s location within the image. In this manner, a low-complexity DNN (with a small number of hidden layers and neurons) can be easily trained into understanding the spatial dependence of the system as a function of the experimental parameters. Most importantly, after training, the simple DNN can be used out-of-the-box to generate new segmented images in a pixel-by-pixel manner for unknown experimental conditions (generalization, i.e. interpolation and extrapolation). This generative functionality is remarkable considering the simplicity of the approach (an uncomplicated DNN with spatial-dependent input). Previously, such functionality has been restricted to highly specialized Convolutional Neural Networks (CNNs) with millions of trainable parameters, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The low complexity of the DNN enables simple/fast modifications/adaptations (e.g. for other projects or in other fields) as well as the use of less-sophisticated computational resources during both training and execution, leading to moderation in power consumption (an increasingly important aspect) while simultaneously simplifying model deployment on mobile devices with limited resources (wherever this aspect may become relevant).

The project focuses on building physically meaningful models, one for each of various physical systems, so that we can predict their behavior under unseen experimental conditions. We target an improved level of extrapolation outside the training range, which CNNs cannot provide (at least yet). Essentially, the difference here is that between data-driven learning (provided by CNNs) and data-driven discovery of underlying physical behavior (our goal).


The position needs to be filled before July 1st.

-Contract duration: 1 year (possibility to extend up to 3 years)
-Estimated annual gross salary: Salary is commensurate with qualifications and consistent with our pay scales
-Target start date: 01/07/2023

MORE INFO: https://dipc.ehu.eus/en/dipc/join-us/physical-modeling-via-deep-neural-networks

This project has been supported by MCIN with funding from European Union NextGenerationEU (PRTR-C17.I1)

Benefits

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