Development and testing of indication driven knowledge based planning using the RapidPlan platform; Development of a comprehensive machine learning based automatic planning platform; Development of quality assurance and reinforced learning methods for maintenance and upgrades to automatic planning systems
MSc in physics, biomedical engineering, computer sciences and engineering (CSE); A good affinity for mathematical modeling with a sound background in machine and statistical learning methods; Previous experience with treatment planning in radiotherapy would be considered an asset for this position; Team player
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We perform cutting-edge research in the fields of matter and materials, energy and environment and human health. By performing fundamental and applied research, we work on sustainable solutions for major challenges facing society, science and economy. PSI is committed to the training of future generations. Therefore, about one quarter of our staff are post-docs, post-graduates or apprentices. Altogether, PSI employs 2100 people. This research project is about an efficient and effective approach for planning high quality and clinically realistic treatment plans, with a minimal need for human interaction, and with turn-around times in the order of minutes. For the Center for Proton Therapy and the Laboratory for Scientific Computing we are looking for a PhD Student Automated Treatment Planning for Proton Therapy Your tasks Development and testing of indication driven knowledge based planning using the RapidPlan platform Development of a comprehensive machine learning based automatic planning platform Development of quality assurance and reinforced learning methods for maintenance and upgrades to automatic planning systems Your profile MSc in physics, biomedical engineering, computer sciences and engineering (CSE) A good affinity for mathematical modeling with a sound background in machine and statistical learning methods Previous experience with treatment planning in radiotherapy would be considered an asset for this position Team player We offer Our institution is based on an interdisciplinary, innovative and dynamic collaboration. You will profit from a systematic training on the job, in addition to personal development possibilities and our pronounced vocational training culture. If you wish to optimally combine work and family life or other personal interests, we are able to support you with our modern employment conditions and the on-site infrastructure. For further information, please contact Dr Tony Lomax, phone: 41 56 310 37 36 or Dr Andreas Adelmann, mail: firstname.lastname@example.org. Please submit your application online by 30 April 2020 (including addresses of referees) for the position as a PhD Student (index no. 1722-00). 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