PhD/Postdoc Positions (m/f/d) in Oncology

The "Next Generation AI-Computing (gAIn)" project aims to address key challenges in AI deployment - energy efficiency, predictability, reliability, and regulatory compliance - through the development of novel AI hardware and software concepts ( ). Goals include significantly reducing the energy footprint of AI applications while achieving algorithmic transparency and satisfying legal requirements such as the "right to explanation."

The research department “Translational Surgical Oncology” ( )” at the National Center for Tumor Diseases Dresden (NCT/UCC Dresden), led by Prof. Stefanie Speidel, conducts research on computer- and robot-assisted surgical systems at the interface of engineering, computer science, and medicine. The goal of the research is to analyze surgical skills and expertise using artificial intelligence (AI), robotics, and multimodal sensor systems, and to support and enhance these capabilities through intelligent assistance systems.

Your Role:As part of this project, you will research and develop techniques to accelerate and optimize AI algorithms for real-time execution on edge devices in the operating room. Your work will build on and contribute to technologies developed within gAIn, advancing methods that are both computationally efficient and clinically deployable. Relevant algorithms include - but are not limited to - edge computing for robotics, video interpretation, imitation learning, and reinforcement learning.

You will collaborate closely within an interdisciplinary team to develop innovative methods that serve as use cases for gAIn and drive methodological progress across the project.

Your Responsibilities:

• Independent research in accordance with good scientific practice: conception and implementation of scientific methods, experiment design, data collection and processing, and authoring publications in peer-reviewed journals
• Presentation of work at international conferences and participation in training and professional development opportunities
• Supervision of student theses and projects
• Participation in project meetings and colloquia
• Support with documentation and reporting
• Onboarding and guidance of research assistants
• Communication of research activities to external stakeholders

• Master's Degree (or equivalent)/PhD in computer science, electrical engineering, applied mathematics or related fields of expertise
• Very good programming skills (e.g. Python, C++)
• Excellent skills and practical experience in one or more of the following research areas is beneficial: Machine Learning/Deep Learning, Robotics, High Performance Computing
• Excellent collaboration skills including interdisciplinary environments
• High curiosity regarding novel machine learning techniques

The position is available as soon as possible on a full-time or part-time basis and is limited until 31 December 2027. Remuneration is based on the classification provisions of the Collective Agreement for the Public Service of the federal states (TV-L) and, subject to the fulfilment of personal requirements, may be assigned to pay grade E13.

• Salary: According to TV-L (pay grade E13, subject to personal qualifications), including annual special payment
• Vacation: 30 days of paid annual leave
• Professional development: Extensive internal and external training opportunities to continuously enhance your skills
• Corporate culture: A transparent, team-oriented environment with shared successes and joint events, including participation in sporting events such as the REWE Team Challenge
• Mobility: Job bike option and subsidy for public transport ticket
• Health: Access to modern sports and fitness programs at our health center, mental health support programs, and comprehensive occupational health management
• Shopping benefits: Employee discounts at the hospital pharmacy, exclusive corporate benefits, and additional shopping platforms
• Work-life balance: Family office support for childcare, holiday programs, and caregiving responsibilities, as well as flexible working models

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