Doctoral student in deep learning for biological systems

SciLifeLab Group; Stockholms kommun; On-site; SEK 290,160 - 334,800; Full time

Job summary

SciLifeLab Group in Stockholm is seeking a Doctoral student in deep learning for biological systems. The successful candidate will contribute to modeling cell-cell interactions to support cancer research and precision medicine. Key qualifications include a master's degree in a related field and a strong background in machine learning, mathematics, and modeling. Candidates should possess research experience and an interest in biological systems. Employment duration is a maximum of four years, under postgraduate education terms.

Qualifications

• Strong background in machine learning, mathematics, and modeling.
• Interest or prior experience with biological systems.
• Proven ability to conduct research and communicate findings.

Responsibilities

• Join a project to model cell-cell interactions to disrupt cancer-promoting equilibria.
• Develop in silico and in vitro models for digital twins of interactions.
• Contribute to research on advancing precision medicine.

Skills

Machine learning

Mathematics

Modeling

Deep learning models

Biological systems

Education

Master's degree in Electrical Engineering or closely related field

120 higher education credits at second-cycle level

Job description

Doctoral student in deep learning for biological systems

School of Electrical Engineering and Computer Science at KTH

Third-cycle subject: Electrical Engineering

The department of Decision and Control Systems (DCS) is currently 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. We aim to develop in silico and in vitro models and tools to build and validate digital twins of such interactions, with the objective of steering cellular systems toward healthier states and transforming drug development to reduce trial‑and‑error and accelerate the translation of discoveries into clinical applications.

DCS conducts fundamental research in machine learning, control systems, and system identification in partnership with AstraZeneca, SciLifeLab, the Karolinska Institute, and collaborators at Caltech, MIT, UC Berkeley, and Stanford. This project will be conducted with Professor Avlant Nilsson’s research group, focusing on advancing precision medicine through models of cancer cells.

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

To be admitted to postgraduate education, the applicant must have basic eligibility in accordance with Swedish Higher Education Ordinance, e.g., a second‑cycle degree or equivalent credits.

Minimum educational requirement: 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). Equivalent knowledge acquired through other means may also be accepted.

Required attributes and skills:

• Strong background in machine learning, mathematics, and modeling.
• Interest or prior experience with biological systems.
• Proven ability to conduct research, communicate findings effectively, and experience with deep learning models.
• Advanced knowledge of machine learning, control theory, or mathematics is highly desirable.

Assessment criteria:

• Independently pursue one’s work.
• Collaborate with others.
• Maintain a professional approach.
• Analyze and work with complex issues.

Employment terms: 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. An employed doctoral student may, to a limited extent (maximum 20%), perform certain tasks such as training and administration. A new position is for a maximum of one year and then may be renewed for up to two years at a time.

Equal opportunity: The position is open to all candidates irrespective of gender, ethnicity, disability, etc.