CVPR 2025 Career Opportunities

Project Summary:

Positron Emission Tomography (PET) is vital for diagnosing diseases like cancer, heart conditions, and neurological disorders. However, conventional PET/CT imaging exposes patients to radiation from tracers and CT scans. This Swiss National Science Foundation (SNSF)-funded project aims to address these challenges by creating data-driven models to enable high-quality, low-dose PET imaging, reducing radiation risks and enhancing accessibility without compromising diagnostic accuracy. This position focus on three main streams: - Denoising of Low-Dose PET Images: Developing models to reduce noise and enhance the quality of PET scans acquired at lower radiation doses. - CT-Free Attenuation Correction: Developing AI-driven techniques for PET imaging that eliminate the need for CT scans, reducing radiation exposure and associated costs. - Robust Image Reconstruction and Synthesis: Using deep learning to create high-quality PET and CT images from low-dose PET data to support accurate, non-invasive diagnostics. This work aims to advance personalized, non-invasive diagnostic techniques, making PET imaging safer and more accessible in clinical practice.

Position Details:

We are seeking a highly motivated PhD candidate to join our team in Switzerland, bringing a strong background in deep learning and a passion for medical imaging. The PhD candidate will play a central role in developing robust and generalizable AI methods to address key technical challenges in PET image reconstruction.

Key responsibilities include:

• Design and implement advanced denoising models for low-dose PET images, focusing on noise reduction while preserving diagnostic detail.
• Develop and validate CT-free attenuation correction techniques that enhance PET imaging without additional radiation exposure.
• Collaborate closely with interdisciplinary teams at the University of Cambridge, Tsinghua University, Lucerne University of Applied Sciences, and Luzerner Kantonsspital to integrate AI solutions into clinical practice.
• Contribute to top scientific publications and outreach activities.
• The project offers access to high-quality medical imaging datasets, a collaborative research environment, and guidance from academic and clinical experts in medical imaging and AI. The selected candidate will be employed for 48 months on the project.

Candidate Requirements:

• Master’s degree in Computer Science, Biomedical Engineering, or a related discipline.
• Strong background in deep learning with practical experience in medical imaging applications preferred.
• Proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow.
• Excellent communication skills in English and an ability to work within international, multidisciplinary teams.
• An eagerness to contribute to impactful research in the field of medical diagnostics.

Benefits:

• Fully funded by the Swiss National Science Foundation (SNSF).
• The opportunity to work within an international research network and to contribute to the development of life-saving imaging technologies.
• Access to state-of-the-art datasets, resources, and facilities, fostering your growth in a supportive and collaborative environment.

Application Process:

Please submit your CV, a cover letter detailing your research experience and motivation, and contact information for two academic references. Applications will be reviewed on a rolling basis until the position is filled.

Contact Information:

For inquiries or to submit your application, please contact: Angelica Aviles-Rivero (aviles-rivero@tsinghua.edu.cn) and Javier Montoya (javier.montoya@hslu.ch)

University of Surrey, Guildford, UK

The sharing of data assets is a major administrative challenge, often causing a roadblock to collaboration and research. This is especially true in sensitive domains like the Nuclear sector, where researchers can struggle to find the data needed to train machine learning models.

This project will develop an AI-driven “secure-by-design” approach to dataset privatisation and distillation, enabling the sharing data assets within the NDA estate, as well as other agencies and academic partners. To achieve this, a framework will be developed that can run on the NDA secure infrastructure, to create a purely synthetic dataset that both obfuscates and compresses while remaining as true as possible to the sensitive historical data.

Academic Institution

This is an opportunity to join the Centre for Vision, Speech and Signal Processing at the University of Surrey. This is the largest such UK institute, and is ranked 1st for Computer vision research in the UK and 3rd in Europe (csrankings.org). The supervisory team includes award winning and world renowned academics, with the applicant joining a large and tightly knit research team of 10+ peers working on various related topics (http://personalpages.surrey.ac.uk/s.hadfield/).

Industrial Sponsorship and Research Impact

This project is funded by the Nuclear Decommissioning Authority (https://ndagroup.careers/about-the-nda-group/). NDA are a government organisation broadly responsible for the safe and sustainable management of historical nuclear sites and materials. This studentship is an ideal opportunity to start-up a career in this important, innovative and growing sector. The project plan includes two separate funded internships, deploying research solutions at sites within the NDA estate, as well as numerous outreach and scale-up activities. This is designed to ensure that the student sees real-world impact from the developments during their PhD studies, and to put them in the best possible position to transfer their research skills into industry after the studentship.

Bath

Lecturer / Senior Lecturer

Department: Computer Science

Salary: Salary for a Lecturer (Grade 8) is £46,735 rising to £55,755 per annum. Salary for a Senior Lecturer (Grade 9) is £57,422 rising to £66,537 per annum

The Department of Computer Science wishes to appoint up to seven academics in Artificial Intelligence and Machine Learning.

About the role

You will work with colleagues, students and researchers to develop and publish papers. You will apply for research funding to support your ideas. You will find ways of making your research available to society.

You will design and deliver teaching materials for lectures, tutorials, and labs.

You will have a few internal roles to help the Department run smoothly.

The support and growth opportunities we provide

Training for an HEA fellowship qualification

New Lecturers will be enrolled into the Pathway to HEA Fellowship. Senior Lecturers will have the option to do so.

Mentoring

All of our staff are allocated a mentor when they join. Your mentor will support you in your day-to-day job and help you progress.

About you

Our ideal candidate for both Lecturer and Senior Lecturer positions will hold a PhD or equivalent in a relevant discipline, along with a UG degree or equivalent experience.

• You should demonstrate substantial research experience in your field, with an emerging track record for Lecturers and an established research profile with funding success for Senior Lecturers
• A deep conceptual understanding of their subject, alongside experience teaching at UG/PG levels, is essential
• Strong written, verbal, and interpersonal skills, along with the ability to form positive collaborations, are required
• Senior Lecturers should also exhibit academic leadership and a clear research vision. Both roles demand excellent organisational and administrative skills
• A commitment to excellence in research and teaching, student experience, and ethical professional conduct is essential

You have solid experience in Computer Graphics with relation to machine learning, e.g., in the areas of neural graphics, rendering, scientific visualization or graphical simulation? You want to deepen your research in a collaborative fashion within a group of closely operating Visual Computing and Sensorics chairs?

In our team we conduct Computer Graphics research in close vicinity to machine learning and computer vision, involving, for instance, range or light-field cameras. We intensively collaborate with Sensorics chairs within the Center for Sensor Systems (ZESS), giving us access to unique sensor and camera expertise and hardware, and with computer vision and machine learning groups that generates a large synergetic research momentum.

We are looking for an enthusiastic and open-minded senior research (an Academic Councilor is similar to a Lecturer/Senior Lecturer) who is eager to conduct Computer Graphics related research and teaching in an interdisciplinary setting and ramp up their own projects. You will be given the opportunity to conduct independent research and teaching in your area of interest, which are ideally related to machine learning, such as neural graphics, rendering, scientific visualization, or graphical simulation. We will support independent activities in the acquisition of third-party funded research projects to foster your own team.

With appropriate performance, the appointment to an adjunct professorship is possible and is supported by the head of the chair. Please apply via https://jobs.uni-siegen.de with job ID 6344, or send an email to andreas.kolb@uni-siegen.de in case you have any question about this open position. Visit our website https://www.cg.informatik.uni-siegen.de/ or the ZESS website https://www.zess.uni-siegen.de to learn more about the working environment.

University of Surrey, Guildford, UK

This research post is part of the multimillion GBP EPSRC-funded SustaPack Prosperity Partnership project between the University of Surrey and Pulpex Ltd. As a research fellow, you will help to make the paper bottle a reality through your fundamental research on autonomous quality control.

Plastic packaging persists in the environment and is difficult to recycle. There is a growing demand for alternative materials to use as containers for liquids. Pulpex Ltd. (https://www.pulpex.com) are developing a new type of bottle made from cellulose fibres. The Pulpex bottle uses sustainable materials, can be recycled in existing paper waste-streams, can naturally degrade if not recycled, and has a carbon footprint 30% less than poly(ethylene terephthalate).

Specialist coatings are needed for the bottles to hold liquids and to enable a long shelf-life for the products contained within them. You will work as part of a team of three post-doctoral fellows and a PhD student at the University of Surrey along with engineers from Pulpex.

The project is well funded to allow training opportunities, travel to use national facilities, and conference attendance. There will be opportunities to visit collaborators’ sites and access national facilities. You will be provided with mentorship for personal and professional development to advance your future career. This project will be an excellent entry into the field of sustainable materials which are rapidly growing in use.