MAASTRO has developed a global partnership of oncology clinics with large repositories of data on cancer, particularly on head and neck cancer. An important part of our research strategy is the Personal Health Train approach (https://vimeo.com/143245835) where artificial intelligence (AI) algorithms can be securely sent between Findable-Accessible-Interoperable-Reusable (FAIR) data stations, to learn personalized cancer treatment decision-making models without broadcasting identifiable personal information.

We are looking for a PhD candidate to develop into a FAIR data expert on large-scale oncological big data, to make valuable clinical data available for machine learning algorithms and thus create decision support systems for specific questions.

In this project, you will be deeply integrated into a multidisciplinary team comprising oncologists, legal experts, medical physicists, software engineers and other clinical data scientists.

We are looking for strong team players who can show a profound and continuous commitment to improving the lives of cancer patients through the use of data and decision support algorithms.

You may be asked to travel onsite to various clinical partners around the world, for up to 3 months at a time. It is expected that you will be willing to travel and present at conferences, international meetings and other means of sharing knowledge.

To be successful in this position, you need be enthusiastic about new scientific challenges, be adaptable and be able to contribute to technical/analytical work as part of a diverse team of professionals.

Specific:

A MSc in a scientific discipline closely allied to clinical data science e.g. biomedical engineering, (medical) physics, computer science, data science, mathematics/statistics, or other similar qualification deemed equivalent by Maastricht University.

Expertise in scripting/programming through R and Python.

Expertise in applications programming and/or container (eg Docker) deployment.

Familiarity with medical data interchange standards (eg DICOM, FHIR, etc).

Experience with data management and FAIR data principles, specifically in relation to Semantic Web.

Proof of academic-level fluency in English (at least IELTS 7 or equivalent). Knowledge of one or more other languages (preferably Dutch, European languages or Asian languages).

Generic / personal :

Demonstrate consistent examples of leadership and mentoring abilities, eg creating a cohesive team, experience with teaching/lecturing, etc.

Excellent scientific writing and public presentation skills.

Excellent interpersonal communication skills.

Show strong interests in clinical decision-making, making cancer care more personal and having empathy for cancer patients.

Fixed-term contract: 4 Years.

The terms of employment of Maastricht University are set out in the Collective Labour Agreement of Dutch Universities (CAO). Furthermore, local UM provisions also apply. For more information look at the website http://www.maastrichtuniversity.nl/, Working at UM.

Maastricht University

Maastricht University is renowned for its unique, innovative, problem-based learning system, which is characterized by a small-scale and student-oriented approach. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools. Maastricht University has around 18,000 students and 4,300 employees. Reflecting the university's strong international profile, a fair amount of both students and staff are from abroad. The university hosts 6 faculties: Faculty of Health, Medicine and Life Sciences, Faculty of Law, School of Business and Economics, Faculty of Science and Engineering, Faculty of Arts and Social Sciences, Faculty of Psychology and Neuroscience.

GROW - School for Oncology and Developmental Biology

GROW focuses on research and teaching of (epi)genetic, cellular and (micro)environmental factors and mechanisms underlying normal (embryonic and fetal) and abnormal (cancer) development, with emphasis on translational research aiming at innovative approaches for individualizing prevention, patient diagnostics and treatment.