Department of Computer Science, IMAGE section invites applicants for a PhD fellowship in real-time radiation dose plan optimization using deep learning, a project financed by the Independent Research Fund Denmark.
Start date is (expected to be) 1st of April 2024 or as soon as possible thereafter.
Radiation therapy is used in more than 50% of cancer patients. Planning the treatment is an image-guided decision of how to deliver the necessary radiation dose to the target tumor while sparing sensitive organs. Current planning processes are both time-consuming and subjective and previous attempts at automation using deep learning have been limited by dataset sizes and to methods that replicate as oppose to improve on current processes. The resulting plans have also been hard to deliver in practice, due to a lack of consideration of the parameters and constraints of the hardware involved.
The overall aim of the project is to develop deep learning methods that can improve, automate and speed up radiotherapy planning and to document the clinical value of such approaches. The proposed project will follow a novel approach of quantifying and optimising plan quality in training of the deep learning model with direct prediction of machine delivery parameters using data from orders of magnitude more patients than prior work in this field. We will investigate if this will lead to improvements over existing clinical procedures and state of the art methods. Artificial intelligence methods need to be tested through prospective evaluations in a real-life clinical scenario. The project is therefore built through both technical aims related to developing the artificial intelligence needed to generate radiotherapy treatment plans and the clinical implementation and testing of such methods in the oncology ward at Rigshospitalet.
Who are we looking for?
We are looking for candidates within the field(s) of computer science, physics, mathematics or related fields. To be eligible to apply for these positions, applicants need to have or be about to obtain an MSc degree in one of these fields (education level options are discussed further below). In addition, the ideal candidate might have
- solid programming experience
- experience in image analysis and machine learning / deep learning
- knowledge of the physics of radiation and radiation treatment
- have a wish to apply advanced computer science and machine learning techniques in medicine
- be creative, solution oriented and able to work both independently and in research teams
- relevant publications
- relevant work experience
- other relevant professional activities
- good language skills, the group is international and fluency in spoken and written English is a requirement
Our group and research- and what do we offer?
The project will be carried out at and is part of an existing cross-disciplinary collaboration between the Image Analysis, Computational Modelling, and Geometry (IMAGE) section at the Department of Computer Science, Faculty of Science, University of Copenhagen and the Radiotherapy Research group at the Department of Oncology, Rigshospitalet. The collaboration aims to combine state of the art image analysis and machine learning tools, expertise, and computational resources with state of the art radiation therapy equipment, knowhow, and large clinical databases recording treatment data and outcomes from one of the largest European radiotherapy departments. Both involved research groups are relatively large, diverse and internationally renowned, each consisting of more than 20 PhDs and Postdocs.
The project will be co-supervised by Professor Ivan Richter Vogelius from the Department of Oncology at Rigshospitalet.
Principal supervisor isAssociate Professor Jens Petersen from the Department of Computer Science, University of Copenhagen. E-mail: firstname.lastname@example.org, Direct Phone: +45 60687733.
The PhD programme
The PhD programme is a three year full-time study within the framework of the regular PhD programme (5+3 scheme).
Getting into a position on the regular PhD programme
Qualifications needed for the regular programme
To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. computer science, physics, mathematics. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.
Terms of employment in the regular programme
Employment as PhD fellow is full time and for maximum 3 years.
Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.
The terms of employment and salary are in accordance to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure.
Responsibilities and tasks in the PhD programme
- Carry through an independent research project under supervision
- Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE
- Participate in active research environments, including a stay at another research institution, preferably abroad
- Teaching and knowledge dissemination activities
- Write scientific papers aimed at high-impact journals
- Write and defend a PhD thesis on the basis of your project
Application and Assessment Procedure
Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.
- Motivated letter of application (max. one page)
- Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position
- Original diplomas for Bachelor of Science or Master of Science and transcript of records in the original language, including an authorized English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted.
- Publication list (if possible)
- Reference letters (if available)
The deadline for applications is 3rd of January 2023, 23:59 GMT +1.
We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.
The further process
After deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified, whether you will be passed for assessment.
The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at http://employment.ku.dk/faculty/recruitment-process/.
Interviews with selected candidates are expected to be held during week 5 and 6.
For specific information about the PhD fellowship, please contact the principal supervisor.
General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website: https://www.science.ku.dk/phd/.
The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position. Part of the International Alliance of Research Universities (IARU), and among Europe’s top-ranking universities, the University of Copenhagen promotes research and teaching of the highest international standard. Rich in tradition and modern in outlook, the University gives students and staff the opportunity to cultivate their talent in an ambitious and informal environment. An effective organisation – with good working conditions and a collaborative work culture – creates the ideal framework for a successful academic career.