Background and Motivation: 

Not all treatment centres will have access to upright imaging modalities, so there is a critical need for alternative approaches. Developing a versatile image guidance workflow, empowered by robust deformable image registration and associated algorithms for generating supine-to-upright image transformations, will be vital to ensure accurate treatment planning in centres with limited resources.

Goal: 

A comprehensive multi-modality image-guided workflow, establishing treatment planning, robustness, and margin guidelines based on the gathered upright imaging data and numerical phantom

Tasks:

• Develop a versatile image analysis framework (including deformable registration, image synthesis and reconstruction) for multi-modality multi-posture images used for upright RT
• Generate anthropomorphic numerical (4D) phantoms for simulating upright anatomy and its possible variations
• Design and in-vivo validate an efficient multi-modal image guidance workflow for upright RT
• 3 months secondment @Therapanacea (synthetic image)
• 3 weeks of research visiting @ASG (4DMR imaging)
• 3 weeks of research visiting @DKFZ (physical phantom design)
• Close collaboration with DC17 and PhDs students in PSI-CPT
• Teaching assistant for ETHz Physics department
• Presentation of research in the form of peer-reviewed publications, reports and oral presentations in workshops, symposiums and conferences
• Participation in Consortium training activities and secondment projects

Education/skill:

• Higher education in Physics, Biomedical Engineering, Computer Science, or a related field 
• Good knowledge of English
• Proven experience in medical image analysis and deep learning frameworks is highly desirable.
• Excellent problem-solving skills, academic writing skills, and communication skills. 
• Ability to work independently as well as in a team. Effective collaboration attitude with both inter- and intra-institutional partners.

Your profile 

• Higher education in Physics, Biomedical Engineering, Computer Science, or a related field  

• Good knowledge of English 

• Proven experience in medical image analysis and deep learning frameworks is highly desirable. 

• Excellent problem-solving skills, academic writing skills, and communication skills.  

• Ability to work independently as well as in a team. Effective collaboration attitude with both inter- and intra-institutional partners. 

Application process 

If you find this position interesting and would like to work in an exceptional, international, strongly innovative environment, please send your full application documents, including the filled application form, motivation letter, short CV, list of most important publications with explanation of your own contribution, and information of your earliest possible starting date. If you are applying for more than one UPLIFT PhD position, you may indicate your top 1-3 preferences in the application form by using the DC numbers associated with the projects. Please submit your application to [email protected] until January 15, 2025. 

We anticipate video conference interviews with candidates starting in the third week of February,
for start dates from March 2025. Applications submitted without any of the required documents will not be considered.  

Candidates can be of any nationality but must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting beneficiary for more than 12 months in the 36 months immediately before their recruitment date. Applicants should be within the first four years of their research careers and must not have been awarded a doctoral degree. Submitted applications will be evaluated in accordance with the European Code of Conduct for Recruitment.