Doctoral student in deep learning for biological systems

KTH Royal Institute of Technology; Stockholms kommun; On-site; SEK 312,480 - 379,440; Full time

Benefits offered by this job

Monthly salary according to KTH’s doctoral student salary agreement

Employment duration of four years with possible renewal

Job summary

KTH Royal Institute of Technology seeks a doctoral student in deep learning for biological systems to model cell interactions affecting cancer. The candidate will work on developing models and tools to enhance drug development, collaborating with institutions like AstraZeneca and SciLifeLab. Required qualifications include a Master’s degree and strong abilities in machine learning, mathematics, and independent research. A diploma related to Electrical Engineering or similar is essential. The position offers a tailored KTH doctoral salary agreement.

Qualifications

• At least 120 higher education credits in Electrical Engineering, Computer Science, Mathematics or closely related fields.
• Interest or previous experience with biological systems is desirable.
• Proven ability to conduct research and work independently.

Responsibilities

• Join a project to model cell‑cell interactions and disrupt cancer-promoting equilibria.
• Develop in silico and in vitro models and tools.
• Work towards transforming drug development.

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

Job description

Doctoral student in deep learning for biological systems

School of Electrical Engineering and Computer Science at KTH

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 in order 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, 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, and the Karolinska Institute. The 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 will supervise the doctoral student.

Admission requirements

To be admitted to postgraduate education, the applicant must meet basic eligibility: either

• passed a second cycle degree (e.g., 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 substantially equivalent knowledge.

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) is required.

Applicants will be assessed on the ability to

• independently pursue their work;
• collaborate with others;
• approach tasks professionally;
• 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. A background in machine learning, control theory or mathematics is highly desirable.

Benefits

Monthly salary according to KTH’s doctoral student salary agreement. Employment is limited to the duration of full‑time doctoral education (four years), with possible renewal for up to two years after the first year.