PhD student in Materials Science

Publiceringsdatum: 2025-09-19

Det är nu möjligt för dig att få jobb som doktorand hos Linköpings Universitet. Linköpings Universitet söker efter en ny anställd på heltid under 6 månader eller längre som är redo att ta sin karriär till nästa nivå. Om du redan bor i närheten av Linköping, är det här din chans att söka ett spännande jobb

Om du tycker att det låter intressant att börja jobba hos Linköpings Universitet i rollen som doktorand så har de just nu en ledig tjänst som skulle kunna vara en perfekt match för dig! Sista ansökningsdagen till jobbet som doktorand hos Linköpings Universitet är fredag den sjuttonde oktober 2025.

Jobbannons

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At the intersection between AI and single atoms.

Your work assignments

We are looking for a PhD student with a background in machine and deep learning with focus on image processing and restoration, to develop novel AI-based approaches to restore and denoise Transmission Electron Microscopy (TEM) images. This position is part of a cross-disciplinary research effort supported by the Wallenberg Initiative Materials Science for Sustainability

(WISE) and Wallenberg AI, Autonomous Systems and Software Program (WASP), a WASP-WISE NEST. This interdisciplinary setting provides a unique opportunity to work at the intersection of AI and experimental science, combining fundamental algorithmic development with real-world applications in scientific imaging.

Due to limitations in electron dose and scan stability, microscopy data is often affected by significant noise and scan distortions. High-quality ground truth data is usually unavailable or costly to collect. To overcome this, we focus on self-supervised denoising, where models learn to restore images using only the noisy data itself — without requiring clean references. Existing approaches often rely on convolutional neural networks (CNNs), which identify local correlations in the images. However, in this project, the aim is to go beyond standard CNN-based methods by developing new approaches based on transformers, and implicit neural representations (INRs). The intention ius also to explore hybrid models that combine the local sensitivity of CNNs with the global modeling capabilities of these emerging architectures. A central goal is to integrate physical priors — including periodicity, symmetry, and long-range correlations — directly into the learning process to achieve more robust, interpretable, and scientifically meaningful reconstructions of microscopy data.

As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time.

Your qualifications

You have graduated at Master’s level in Computer Science, Materials Science, Physics, Mathematics, or a related disciplines, or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in Computer Science, Physics, or Mathematics. Alternatively, you have gained essentially corresponding knowledge in another way.

Experience in one or more of the following areas is considered meritorious, Self-supervised learning, image denoising, or inverse problems, Transformer-based architectures, Scientific image or microscopy data analysis.

You should possess excellent analytical skills, a genuine interest in interdisciplinary research, and the ability to work both independently and as part of a team. You are expected to have good communication skills in English, both spoken and written. You are self-motivated, communicative, helpful and committed to your work.

Your workplace

You will be formally based in the Electron Microscopy of Materials (EMM) unit within the Thin Film Physics division at the Department of Physics, Chemistry and Biology (IFM). EMM conducts leading research in advanced electron microscopy for materials science and nanotechnology, with access to state-of-the-art instrumentation for HAADF-STEM imaging and in situ experiments.

The project will be pursued in close collaboration with the Materials Design Division (also at IFM) and with the Computer Vision Laboratory at the Department of Electrical Engineering (ISY), a world-class research environment specializing in machine learning, deep learning, and visual perception.

The employment

When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral studies at each faculty is available at Doctoral studies at Linköping University

The employment has a duration of normally four years’ full-time equivalent. Extension of employment up to five years is based on the degree of teaching and institutional assignment. Further extensions may be granted in exceptional circumstances. You will initially be employed for one year, after which your employment will be renewed for a maximum of two years at a time, depending on your progress through the study plan. 

Starting date by agreement. 

Salary and employment benefits

The salary of PhD students is determined according to a locally negotiated salary progression.

More information about employment benefits at Linköping University is available here.

Union representatives

Information about union representatives, see Help for applicants.

Application procedure

Apply for the position by clicking the “Apply” button below. Your application must reach Linköping University no later than 17th of October 2025.

Applications and documents received after the date above will not be considered.

We welcome applicants with different backgrounds, experiences and perspectives - diversity enriches our work and helps us grow. Preserving everybody's equal value, rights and opportunities is a natural part of who we are. Read more about our work with: Equal opportunities.

We look forward to receiving your application!

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