The School of Electrical Engineering and Computer Science at KTH 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. 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, and the Karolinska Institute. We collaborate with researchers at Caltech, MIT, UC Berkeley, 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 is proposed to supervise the doctoral student. Decisions are made on admission.
To be admitted to postgraduate education, the applicant must have basic eligibility in accordance with either of the following:
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. Equivalent knowledge acquired through other means may also be accepted.
In addition to the above, there is also a mandatory requirement for English equivalent to English B/6.
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:
Interest or previous experience with biological systems, proven ability to conduct research, communicate findings effectively and experience with deep learning models is desirable. Great emphasis is placed on study results and completed courses. Earlier specialization in machine learning, control theory or mathematics is highly desirable.
After the qualification requirements, great emphasis will be placed on personal skills.
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.
As a doctoral student, you are entitled to a workplace with many employee benefits and monthly salary according to KTH's Doctoral student salary agreement. Read more about Doctoral studies (PhD) | KTH | Sweden.
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 include the following elements:
Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/Central European Summer Time).
For information about processing of personal data in the recruitment process.
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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Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.
Tagged as: Engineering, Physics
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