项目介绍

2025年5月8日

Third-cycle subject: Electrical Engineering

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.

What we offer

• The possibility to study in a dynamic and international research environment in collaboration with industries and prominent universities from all over the world.
• A workplace with many employee benefits and monthly salary according to KTH’s Doctoral student salary agreement.
• 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.
• Guidance on relocating and settling in at KTH and in Sweden

Admission requirements

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 addition to the above, there is also a mandatory requirement for English equivalent to English B/6.

Selection

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. 

Union representatives

Contact information forunion representatives.

Doctoral section (Students’ union on KTH Royal Institute of Technology)

Contact information fordoctoral section.

To apply for the position

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.

For information about processing of personal data in the recruitment process.