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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.
In this project, we 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 PhD scholar will work independently and in collaboration with Prof. Erik Sonnhammer's group at SciLife Lab and Assist. Prof. Martina Scolamiero's group at KTH Mathematics department.
Supervision: Associate Professor Saikat Chatterjee is proposed to supervise the doctoral student. Decisions are made on admission.
A postgraduate education at an institution that is active and supportive in matters pertaining to working conditions, gender equality and diversity as well as study environment.
Work and study in Stockholm, close to nature and the water.
To be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:
passed a second cycle degree (for example a master's degree), or
completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or
acquired, in some other way within or outside the country, substantially equivalent knowledge
In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, candidates will be assessed upon their ability to:
independently pursue his or her work
collaborate with others,
have a professional approach and
analyze and work with complex issues
The candidate should be able to present prior master student level knowledge in linear algebra, probability theory, deep learning, machine learning, generative AI and their practical implementations.
Added knowledge in mathematical topics like graphs and topologies will be helpful and is considered a plus.
The candidate must show interest to learn life science data analysis and their use in medical science as well as demonstrating knowledge in relevant coding softwares like Python / Matlab, and interest to perform hands-on coding experiments.
After the qualification requirements, great emphasis will be placed on personal skills.
Target degree: Doctoral degree
Information regarding admission and employment
Only those admitted to postgraduate education may be employed as a doctoral student. The total length of employment may not be longer than what corresponds to full-time doctoral education in four years' time. An employed doctoral student can, to a limited extent (maximum 20%), perform certain tasks within their role, e.g. training and administration. A new position as a doctoral student is for a maximum of one year, and then the employment may be renewed for a maximum of two years at a time.
Apply for the position and admission through KTH's recruitment system. It is the applicant’s responsibility to ensure that the application is complete in accordance with the instructions in the advertisement.
Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/Central European Summer Time).
Applications must include the following elements:
CV including your relevant professional experience and knowledge.
Copies of diplomas and grades from previous university studies and certificates of fulfilled language requirements (see above). Translations into English or Swedish if the original document is not issued in one of these languages.Copies of originals must be certified.
Representative publications or technical reports. For longer documents, please provide a summary (abstract) and a web link to the full text.
Other information
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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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 according to KTH's doctoral student salary agreement Number of positions: 1 Full-time equivalent: 100% City: Stockholm County: Stockholms län Country: Sweden Reference number: PA-2025-1297 Contact:
