福利姬

The spatial arrangement of nanoparticles on porous materials is critically important across many applications. However, there is currently no method to quantitatively describe this spatial arrangement. In a collaboration between the (computational mathematics, Stockholm University) and the CATSA group (materials science, Link枚ping University), we aim to develop methods to quantify such spatial arrangements. We now seek an outstanding postdoc to lead our efforts.

Why join us?

You will continue to develop your expertise in computational mathematics, data science and machine learning for image recognition. Moreover, you will develop methods and software that will allow new characterization of nanoscale materials. Therefore, your research will have great impact in academia and industry.

Your research direction and work assignments

You will develop methods, based on computational geometry, to generate spatial information of nanoparticles on irregular 2D surfaces in 3D space.

One powerful experimental method to qualitatively probe spatial organization of nanoparticles in porous materials is electron tomography, whereby 3-dimensional images (tomograms) can be generated. One of your main goals will be to design methods and tools to provide quantitative information of nanoparticle spatial distributions in these tomograms.

Questions that may be explored during your postdoc include:

• What are efficient machine learning strategies to identify large ensembles of nanoparticles in tomograms (i.e., to identify nanoparticles on irregular 2D surfaces in 3D space)?
• What are appropriate descriptors of spatial distribution in the field of materials science (e.g., Voronoi tessellations, particle-particle distances, etc.)?
• What are appropriate algorithms for efficient quantification of spatial distribution in electron tomograms?

Examples of work that you may conduct during your postdoc:

• Algorithm development and implementation (e.g. in C++).
• Machine learning development for image recognition.
• Transformation of tomographic voxel representations into triangulated surfaces (2-manifold triangular meshes).
• Collaboration with experimentalists to ensure real world impact of your developed computational tools / software.

Your postdoc work will be crucial to the broader development of new mathematical tools for materials characterization that are pursued both in the Hellmuth group and in the CATSA group. This broader development will be done in a multidisciplinary team of mathematicians, data scientists, material scientists, and physicists.

Your workplace and research environment

This Postdoc position will be jointly based in the Hellmuth group at the department of mathematics, Stockholm University, and in the CATSA group at the department of science and technology, Link枚ping University. You will spend most of your research time in the Hellmuth group with frequent collaborative meetings with the CATSA group, and you will be advised both by Marc Hellmuth and by Alexander Holm.

Research in the Hellmuth group bridges the gap between computer science, mathematics, and experimental research. Our research focus on the mathematical characterization of discrete data and on the development of mathematical theory to understand complex problems in the life- and material sciences. More information about Marc Hellmuth and his group can be found .

Research in the Catalysis and Self-Assembly (CATSA) group focus on fundamental and applied aspects of heterogeneous catalysis to make sustainable materials and chemicals. We also develop new characterization tools, especially in the field of electron tomography. Information about our research can be found here. Information about Alexander Holm can be found here.

Qualifications

By the start date, you have graduated with a PhD in mathematics, computer science, physics, materials science, or related fields.

You must have proven expertise in at least one of the following fields: computational geometry, algorithm development, machine learning for image recognition. The required expertise could be demonstrated through peer-reviewed papers, papers in review, conference proceedings, software developed, courses taught, etc.

Furthermore, you should be highly self-motivated and show profound interest in fundamental research. Great emphasis will also be placed on personal qualities and suitability.

The employment

This employment is a temporary contract of two years. The employment is full-time.

Starting date spring 2026 or by agreement.

Salary and employment benefits

The university applies individual salaries. Postdoc salaries start around 42 000 SEK / month before tax.

As an employee of Link枚ping University, you are entitled to the following benefits:

• Paid vacation (28 workdays per year, corresponding to 5 陆 weeks).
• Free access to the Swedish health-care system (full health-care coverage).
• Paid maternity / paternity leave (up to 390 days per child).
• Accrued pension. During your work-time in Sweden, you accrue income pension, paid by the employer, which is payable also if you leave Sweden.
• More information about employment benefits at Link枚ping University is available here.
• More information on the (free) Swedish social security system can be found

Union representatives

Information about union representatives, see Help for applicants.

Application procedure

Apply for the position by clicking the 鈥淎pply鈥� button. Your application must be received no later than December 1, 2025. 
 
Applications and documents received after the date above will not be considered. 

The application should be written in English and include:

1. Cover letter (maximum 1 page): The cover letter should describe what makes you interested in, and suitable for, this particular position.
2. CV: The CV can be formatted according to your own design. It should include at least information on education (degrees including topics, schools/universities, years, and preferably average grades), publications (stating all information, including full author lists), any other merits you feel are relevant (skills, awards, etc.).
3. Undergraduate and graduate transcripts (i.e., MSc, BSc, or equivalent, in English), with grades.