PhD Fellowship in Deep Learning, University of Copenhagen, Denmark

PhD Fellowship in Deep Learning: The Department of Computer Science, Faculty of Science, University of Copenhagen, invites applications for a PhD fellowship in real-time radiation dose plan optimization using deep learning. The project, funded by the Independent Research Fund Denmark, aims to improve, automate, and expedite radiotherapy planning through the development and application of deep learning methods. The fellowship is expected to commence on April 1, 2024, or as soon as possible thereafter.

Designation: PhD Fellow

Research Area: Radiation Dose Plan Optimization Using Deep Learning

Location: Department of Computer Science, University of Copenhagen, Denmark

Eligibility/Qualification:

• MSc degree in computer science, physics, mathematics, or related fields.
• Solid programming experience.
• Experience in image analysis and machine learning/deep learning.
• Knowledge of the physics of radiation and radiation treatment.
• Interest in applying advanced computer science and machine learning techniques in medicine.
• Creative, solution-oriented, able to work independently and in research teams.
• Relevant publications and work experience.
• Proficiency in spoken and written English.

Job Description:

PhD Fellowship in Radiation Dose Plan Optimization Using Deep Learning

• Full-time, three-year PhD program within the regular PhD program (5+3 scheme).
• Develop deep learning methods for real-time radiation dose plan optimization.
• Collaborate with the Image Analysis, Computational Modelling, and Geometry (IMAGE) section and the Radiotherapy Research group.
• Combine image analysis and machine learning tools with radiation therapy equipment and large clinical databases.
• Co-supervised by Professor Ivan Richter Vogelius and Associate Professor Jens Petersen.

How to Apply: Interested candidates must submit their applications electronically through the online application portal by January 3, 2024, 23:59 GMT +1. The application should include a motivated letter of application, curriculum vitae, original diplomas, publication list, and reference letters if available.

Last Date for Apply: 03 January 2024

Link

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