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Ikerbasque Eusko Jaurlaritza

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This job offer was closed at 2023-06-01

DIPC - Donostia International Physics Center

PhD Student in "Physical modelling via deep neural networks"

Research Field: Physics 
Sub Research Field:  

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)
DEADLINE: 01/06/2023

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


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FP7 / PEOPLE / Marie Curie Actions

Research Framework Programme/
Marie Curie Actions
SESAM Agreement Number  

Job Details

Type of Contract Temporary 
Status Full-time 
Hours Per Week 40  
Company/Institute Fundación Donostia International Physics Center 
Country SPAIN 

Organization/Institute Contact Data

Organization DIPC - Donostia International Physics Center 
Organization/Institution Type Research Laboratory 
Faculty/Department/Research Lab  
Country SPAIN 
City Donostia - San Sebastian 
Postal Code E-20018 
Street Paseo Manuel de Lardizabal, 4 
Phone +34 943 01 51 21 
Mobile Phone  

Application Details

Envisaged Job Starting Date 2023-07-01 
Application Deadline 2023-06-01 
How To Apply e-mail