We are looking for a Postdoc 'AI/computational biology for cancer research on uveal melanoma'. The aim of our project is to improve our mechanistic understanding of the immunological processes critical for response or underlying resistance to immunotherapy, to guide hypothesis-driven small clinical trials in the future. Will you join our team?

Uveal melanoma is a rare, highly aggressive cancer originating from melanocytes in the eye. There are no standard treatment options available for uveal melanoma. In particular, novel immunotherapy treatments that are effective in other types of cancer, including cutaneous (skin) melanoma, seem to work less well in uveal melanoma. This lack of response might be due to the immune-privileged status of the eye: immune cells are usually not able to enter the eye.

Therefore, in this project, funded by a grant from the Hanarth foundation, we will study the immune landscape of uveal melanoma using multiple types and sources of data. Use of heterogeneous, observational datasets is unavoidable when studying rare cancers, but comes with a high risk of confounding and selection biases. Our project will:

• Use in silico simulations to identify such biases upfront, quantify their potential magnitude, and thoroughly pre-plan appropriate statistical analyses.
• Integrate the heterogeneous types of data in a joint statistical model to account for confounding and selection biases and will provide critical guidance in understanding the reliability and robustness of our conclusions.

You will be jointly affiliated with the Data Science group of the Institute for Computing and Information Sciences (iCIS). Research at iCIS focuses on three themes: software science, digital security and data science. These themes build upon Radboud University's long-standing tradition of combining cutting-edge research on the mathematical foundations of computer science with societally relevant problems that are susceptible to scientific solutions. During recent evaluations, iCIS has been consistently ranked as the No. 1 Computing Science department in the Netherlands. Evaluation committees praise our flat and open organisational structure and our ability to attract external funding.

The focus of the research of the Data Science section lies on machine learning and artificial intelligence in general. The section is involved in various projects with other groups, both within and outside the Radboud University. Our focus areas of expertise are deep learning, AI, information retrieval, and biomedical data science.

Tasks and responsibilities

• Develop and apply methods for generating simulated data to develop and optimize data analysis protocols.
• Improve our custom deep learning pipeline for processing imaging data.
• Develop and apply data fusion techniques to available, heterogenous datasets.
• Actively participate in multidisciplinary scientific meetings.

We are looking for a motivated postdoctoral researcher who wants to focus on the improvement of immunotherapy in patients with uveal melanoma. You have a PhD degree and excellent track record in a relevant field (including, but not limited to, computational biology, AI, data science, molecular biology, immunology, oncology, bioinformatics, statistics, computer science, physics, or mathematics). You will work with clinical and translational research data (flow cytometry, sequencing, multiplex immunohistochemistry imaging). We are looking for a candidate that is/has:

• Strong analytical skills.
• Experience in or willingness to learn programming, with proficiency in data science-oriented languages such as R and python considered a plus.
• Experience in or willingness to learn modelling and/or simulation methodologies, such as differential equations, agent-based models, or generative machine learning models.
• Able to work both independently and in a team.
• A strong interest in translational research.
• Interpersonal and communication skills to effectively collaborate and communicate in an interdisciplinary research setting.
• A high level of proficiency in English.

We will consider qualified applicants without experience in programming and/or machine learning provided they are willing to learn the required skills on the job. Embedding in the computational immunology group would ensure that you get the support you need in acquiring the necessary skills.

Fixed-term contract: 3 years.

Working at Radboud university medical center means that you are ahead of the curve and working together on the healthcare of the future. And there is more. Our secondary terms of employment are impressive. These are fully tailored to you thanks to our Employment Conditions Selection Model. At Radboud university medical center, you will be given trust, and you will take the responsibility to handle everything together. We provide annual courses, both professional and personal.

• In addition to your monthly salary and an annual vacation allowance of 8%, you will receive an end-of-year bonus of 8.3%.
• If you work irregular hours, you will receive an allowance.
• As a full-time employee (36 hours per week), you are entitled to approximately 168 vacation hours (over 23 days) per year.
• Radboud university medical center pays 70% of the pension premium. You pay the rest of the premium with your gross salary.
• You get a discount on health insurance as well: you can take advantage of two group health insurance plans. UMC Zorgverzekering and CZ collectief.

In addition to our terms of employment, we also offer employees various other attractive facilities, such as childcare and sports facilities. Want to learn more? Take a look at the CAO UMC.

Radboudumc

This project is a collaboration between the Medical Oncology department at the Radboudumc and the Data Science group at the Radboud University, and is jointly led by Dr. Kalijn Bol, medical oncologist specialized in melanoma and immunotherapy, and Dr. Johannes Textor, associate professor of machine learning for data science and leader of the computational immunology group. You will be employed at the Medical Oncology department at Radboudumc, but you will also be embedded in a strong machine learning research group at Radboud University, with supervision shared equally between both sides. This setup will provide a unique opportunity for professional and personal growth.

The unique and excellent infrastructure of the department of Medical Oncology places emphasis on working in multidisciplinary teams to improve both patient care as well as research. The nursing department, day care and outpatient clinic are all integrated to guarantee a well streamlined process in the treatment for our patients. Embedded in our academic patient care are early stage clinical trials, and translational research regarding pharmacology, pathology, genetics, radiology, nuclear medicine and tumour immunology. We have a close relation with the department of Tumor Immunology focussing on translational research. The department of Tumor Immunology aims at a better understanding of our immune system using molecular-, cell biological- and immunological- techniques.

The Radboudumc aims to excel in all academic tasks and in giving the patient an important voice in the treatment team. To act together within networks and partnerships are topical issues of the Radboudumc subjects for giving shape to an optimal organization of sustainable oncology care.

Radboudumc
Radboud university medical center is a university medical center for patient care, scientific research, and education in Nijmegen. Radboud university medical center strives to be at the forefront of shaping the healthcare of the future. We do this in a person-centered and innovative way, and in close collaboration with our network. We want to have a significant impact on healthcare. We want to improve with each passing day, continuously working towards better healthcare, research, and education. And gaining a better understanding of how diseases arise and how we can prevent, treat, and cure them, day in and day out. This way, every patient always receives the best healthcare, now and in the future. Because that is why we do what we do.

Read more about our strategy and what working at Radboud university medical center means. Our colleagues would be happy to tell you about it. #weareradboudumc