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PhD Student in "Physical modelling 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.

Physical modelling via generative low-complexity deep neural networks with position-dependent input.

Job Description

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).

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 (generalisation, 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 specialised 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 successful candidate is expected to perform a number of tasks, including (A) physical modelling of various systems, (B) automated image segmentation, (C) optimization of the network structure, (D) improved extrapolation. Previous experience in DNNs is required. In particular, direct experience in the use of one or several open-source software libraries is a must (TensorFlow, PyTorch, Keras, Caffe, etc…). TensorFlow is favoured, since the already existing code is based on it. Experience in other Machine learning (ML) techniques, such as support vector machines (SVMs), decision trees (DTs), etc… will be appreciated but secondary.

Experience/interest in Physics/Mechanical engineering/Numerical modelling will be highly appreciated. Experience/interest in extrapolation techniques will be highly appreciated as well. The ability of neural networks in general to generalise outside the range where they were trained is currently very weak. The candidate should demonstrate excellent/advanced proficiency in both written and spoken English to ensure a smooth collaboration with the Chinese group.

DURATION: 1 year (possibility to extend up to 3 years)
ESTIMATED STARTING DATE: 1 July 2023
DEADLINE: 01/06/2023
APPLICATION EMAIL: jobs.research@dipc.org

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

Benefits

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