PhD-student on distributed deep learning to reduce side-effects from head-and-neck cancer treatments

You cannot apply for this job anymore (deadline was 20 Dec ’19)

Please note: You cannot apply for this job anymore (deadline was 20 Dec ’19). Browse the current job offers or choose an item in the top navigation above.

PhD-student on distributed deep learning to reduce side-effects from head-and-neck cancer treatments

For our Data Science research division, we are looking for a 'PhD-student (36 hours/week)' on distributed deep learning to reduce side-effects from head-and-neck cancer treatments

Deadline Published Vacancy ID SP PhD deep learning

Academic fields

Natural sciences; Health; Engineering

Job types

PhD

Education level

University graduate

Weekly hours

36 hours per week

Salary indication

€2336—€2731 per month

Location

Doctor Tanslaan 12, 6229 ET, Maastricht

View on Google Maps

Job description

The research division of Data Science has three research themes: build global data sharing infrastructures, learn cancer outcome prediction models from this data and apply outcome prediction models to improve lives of cancer patients.

In this position, you will develop into a specialist in distributed Deep Learning that can efficiently use Findable, Accessible, Interoperable and Reusable (FAIR) oncology data from around the world. Clinical data and radiological images will be brought together as a decision support system to help reduce surgical dissections of the neck following chemo/radiation treatment. You will be part of a flexible multidisciplinary environment comprising oncologists, legal experts, medical physicists, software engineers and other clinical data scientists.

Requirements

We are looking for a team player who has demonstrated a deep commitment to improving cancer care. We require a Masters degree (or equivalent) in a scientific discipline closely related to clinical data science and machine learning, with a strong background in Python programming and Docker deployment.

Willingness to travel for research work, teaching/mentoring experience and knowledge of multiple European/Asian languages will be viewed highly favourably. You must be willing to relocate to the region around Maastricht (which is also close to Germany and Belgium). Knowledge of the Dutch language is not a requirement, but you must be fluent in English (better than IELTS 7, or equivalent) and have excellent communication skills for academic publications and oral presentations.

Conditions of employment

Fixed-term contract: 1 year, with a possible extension to 4 years.

We offer employment conditions and salary based on the Dutch Collective Labor Agreement for Hospitals (CAO-Ziekenhuizen). You will receive a fulltime contract (36 hours/week) for an initial period of one year, which in case of a successful evaluation will be extended to a total period of 4 years. As a PhD-student, your salary will be according to the salary scale FWG 50 (min. € 2.336,-, max. € 2.731,-).

Fringe benefits include a 8.33% holiday allowance, a 8.33% end-of-year bonus and an excellent pension provision. In addition, Maastro offers various discount schemes with regard to (healthcare) insurance, bicycle purchase and sports subscriptions. Candidates from abroad may qualify for the advantageous 30% tax rule and a reimbursement of relocation expenses (conditions apply).

Employer

The internationally acclaimed radiotherapy institute Maastro in Maastricht delivers cancer care in the Limburg region of the Netherlands, aiming to cure patients while preventing side effects of the provided treatments. All disciplines within Maastro contribute to this endeavour to optimally prepare and perform the treatments. Maastro is a state-of-the-art clinic with the latest imaging and radiotherapy equipment, and has started treating patients with proton therapy beginning 2019.

Furthermore, we have well-established research groups and we work closely with MaastrichtUniversity (Research School GROW) and Maastricht University Hospital (MUMC+) in the fields of education, clinical and pre-clinical research.