Doctoral student in deep learning for biological systems

Karlstad University

Stockholms kommun

On-site

SEK 323,640 - 379,440

Full time

14 days+

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Job summary

KTH Royal Institute of Technology in Stockholm is hiring a Doctoral Student in Deep Learning for Biological Systems. Candidates need a master's degree or equivalent and relevant higher education credits in Electrical Engineering or related fields. The role involves research on cell-cell interactions, requiring skills in machine learning and deep learning models. The position is temporary and offers competitive compensation according to KTH’s agreements. Applications are due by July 31, 2026.

Qualifications

Passed a second cycle degree or equivalent qualifications.
At least 120 higher education credits in relevant fields required.
Mandatory English proficiency equivalent to English B/6.

Responsibilities

Join a project to model cell-cell interactions in biological systems.
Develop in silico and in vitro models for drug development.
Collaborate with leading researchers in the field.

Skills

Machine learning

Mathematics

Modeling

Deep learning models

Collaboration

Research communication

Education

Master’s degree or equivalent

120 higher education credits at second-cycle level in relevant fields

Job description

Doctoral student in deep learning for biological systems

KTH Royal Institute of Technology in Stockholm, founded in 1827, has grown to become one of Europe’s leading technical and engineering universities.

Third-cycle subject: Electrical Engineering

Department of Decision and Control Systems (DCS) is seeking a doctoral student with a strong background in machine learning, mathematics, and modeling, and an interest in biological systems. The successful candidate will join a project to understand and model cell‑cell interactions to disrupt cancer‑promoting equilibria. The goal is to develop in silico and in vitro models and tools to build and validate digital twins of such interactions, steering cellular systems toward healthier states and accelerating drug development.

DCS conducts fundamental research in machine learning, control systems, and system identification in partnership with AstraZeneca, SciLifeLab, and the Karolinska Institute, collaborating with researchers at Caltech, MIT, UC Berkeley, and Stanford.

Supervision: Prof. Matthieu Barreau, Alexandre Proutiere, Anna Herland, and Avlant Nilsson

Admission Requirements

Applicants must meet one of the following eligibility criteria under Swedish Higher Education Ordinance:

passed a second cycle degree (e.g., master’s degree)
completed course requirements of at least 240 higher education credits, of which at least 60 second‑cycle credits
acquired substantially equivalent knowledge through other means

At least 120 higher education credits at second‑cycle level or higher in Electrical Engineering or closely related fields (e.g., Computer Science, Mathematics, Mechanical Engineering) are required. Equivalent knowledge may be accepted.

Mandatory English proficiency equivalent to English B/6 is required.

Selection Criteria

During the selection process candidates are assessed on their ability to:

independently pursue their work
collaborate with others
maintain a professional approach
analyze and work with complex issues

Interest or previous experience with biological systems, proven ability to conduct research, communicate findings effectively, and experience with deep learning models is desirable. Earlier specialization in machine learning, control theory, or mathematics is highly desirable.

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 exceed the full‑time doctoral education period of four years. A new doctoral student position is for a maximum of one year and can be renewed for up to two years at a time. The student's role may involve up to 20% of training and administrative tasks.

The position offers a competitive monthly salary in accordance with KTH’s doctoral student salary agreement.

Application Process

Applications must include:

Copies of diplomas and grades, and certificates of language proficiency (translations into English or Swedish if necessary). Certified originals required.
CV detailing relevant professional experience and knowledge.
Application letter (max 2 pages) explaining research interests and future goals.
Representative publications or technical reports (with abstracts and links where applicable).
Contact information for at least three references.

Applications must be received by midnight, CET on the last closing date: 31 July 2026.

Job Details