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We have witnessed spectacular successes including Nobel prize in developing artificial intelligence (AI) methods for estimating complex biological structures such as protein structures. This is achieved by harnessing power of deep neural networks (DNNs) and generative AI (GenAI). The AI methods typically use supervised learning that requires a large amount of labeled data. For example, AlphaFold used protein sequence-and-structure as labeled data kept in Protein Data Bank. In addition, the protein sequence is a (relatively) clean data without much noise.
There are abundant unlabeled and noisy data in research fields of modern biology and medical science. Naturally, estimating biological structures and networks from unlabeled and noisy data using unsupervised and semisupervised learning widens the scope of future AI-based research in biology. This has directly actionable effects in medical science. The major technical challenge is development of robust AI methods that can use information hidden in unlabeled and noisy data. A promising path to address the challenge is to include a-priori biological knowledge in developing models for signals and systems, and collecting data, and then regularize the learning of AI methods.
This postdoc project will develop biology informed robust AI and generative AI methods that can use the abundant unlabeled and noisy data. We will focus on inference of gene regulatory networks (GRNs) from their noisy gene expression level data - a challenging inverse problem in biology. The project is challenging and part of Digital Futures Flagship project "Biology-informed Robust AI Methods for Inferring Complex Gene Regulatory Networks". The postdoc scholar will work independently and in collaboration with Prof. Erik Sonnhammer's group at SciLife Lab and Assist. Prof. Martina Scolamiero at KTH Mathematics department.
What we offer
A position at a leading technical university that generates knowledge and skills for a sustainable future
Engaged and ambitious colleagues along with a creative, international and dynamic working environment
A doctoral degree or an equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made.
Here you fill in additional requirements for the position, in case of any language requirements, justification is required. Contact HR if you have any questions.
A doctoral degree in electrical engineering, computer science, data science, mathematics or a related field is required.
Preferred qualifications
A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
Prior theoretical knowledge in linear algebra, probability theory, deep learning, machine learning, generative AI like generative adversarial networks (GAN), normalizing flows, diffusion models, etc., and their practical implementations.
Added knowledge in mathematical topics like graphs and topologies will be helpful.
Interest to learn life science data analysis and their use in medical science.
Knowledge in relevant coding softwares like Python / Matlab, and interest to perform hands-on coding experiments.
In addition, we prefer that the person is competent with following qualifications.
Research expertise
Teaching abilities
Awareness of diversity and equal opportunity issues, with specific focus on gender equality
Log into KTH's recruitment system to apply for this position. You are responsible for ensuring that your application is complete according to the instructions in the ad.
The application must include:
CV including relevant professional experience and knowledge.
Publication list.
Copy of diplomas and grades from your previous university studies. Translations into English or Swedish if the original documents have not been issued in any of these languages.
Brief account of why you want to conduct research, your academic interests and how they relate to your previous studies and future goals. Maximally two pages long.
Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).
About the employment
The position offered is for, at the most, three years.
A position as a postdoctoral fellow is a time-limited qualified appointment focusing mainly on research, intended as a first career step after a dissertation.
Others
Striving towards gender equality, diversity and equal conditions is both a question of quality for KTH and a given part of our values.
It may be the case that a position at KTH is classified as a security-sensitive role in accordance with the Protective Security Act (2018:585). If this applies to the specific position, a security clearance will be conducted for the applicant in accordance with the same law with the applicant's consent. In such cases, a prerequisite for employment is that the applicant is approved following the security clearance.
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Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.
About KTH
KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy. Read more here
Type of employment: Temporary position Contract type: Full time First day of employment: According to agreement Salary: Monthly salary Number of positions: 1 Full-time equivalent: 100% City: Stockholm County: Stockholms län Country: Sweden Reference number: PA-2025-1330 Contact:
KTH Royal Institute of Technology (KTH) is a true innovator that provides you with an opportunity to shape the world through your work. As one of the world’s...
