Machine Learning in Medical Imaging
Philips / Eindhoven (NL)Apply on site
Start date: We prefer students able to start as soon as possible, no later than Q3 2021
Duration: 5-6 months
We can support you with your graduation/thesis research depending on the subject you will bring to the table. If graduation/thesis is supported, we require you to work 32 hours per week on the project(s), otherwise 40 hours per week will be the standard.
In this role, you have the opportunity to
Collaborate with several partners in Europe, amongst other universities from Valencia, Turin and Modena and several hospitals around Europe. In this cooperation there are two libraries developed, the European Computer Vision Library (ECVL) and the European Distributed Deep-Learning Library (EDDLL). Together, these libraries provide capabilities to work on Machine Learning algorithms. These self-learning algorithms operate on images gathered from Medical image scanners. Amongst the use-cases in the project are those that visualize each part of the anatomy with a different color. For this project, Philips will create end-user applications that use these algorithms in the clinical practice. Therefore, actual doctors are the end-users of the applications.
You are responsible for
- Develop specializations or customize the solution to one or more use cases
- Define detailed requirements
- Implement solutions based on those requirements
- Verify the solution
- Provide the solution to the hospital
You are a part of
The Health Suite Clinical Platform group of Philips based at the High Tech Campus (building 36) in Eindhoven.
Due to the current COVID19 situation, we do support you working from a home-based situation. In case the situation will change, allowing us to return to the offices, your Philips mentor will inform you.
To succeed in this role, you should have the following skills and experience
- Currently studying towards your Bachelor or Master study focusing on Software Engineering
- Web development experience, preferably using Angular
- [Optional] Experience with image processing and/or machine learning
- Team player: Technology focused with purpose to contribute to a better world, Hands-on mentality, sufficient amount of independency
- Fluent in oral and written English
In return, we offer you
Ability to work in one of the leading Healthtech companies and get hands-on experience with Deep Learning Neural Networks. You will work with cutting edge Web technologies, using SCRUM and Agile daily. Participate in backlog refinement sessions, in an international setting, with colleagues of various nationalities.
Why should you join Philips?
Working at Philips is more than a job. It’s a calling to create a healthier society through meaningful work, focused on improving 3 billion lives a year by delivering innovative solutions across the health continuum. Life at Philips is an opportunity for personal and professional growth. And a journey into the unexpected; our people often experience moments when their lives and careers come together in meaningful ways.
In addition to being purpose-driven, we deeply believe in equality and that our people should be a reflection of the society and countries in which we operate. So we value our people in all aspects of diversity, whether generational, gender, experience, ethnicity, race, sexual orientation, ability, nationality, or other aspects. We believe that a flexible and inclusive culture invites a full spectrum of ideas, opinions, and experiences, and strive to create it wherever possible.
Required documents in order for us to continue with your application:
- Grade lists
Please note that in order to be considered for an internship, you need to be registered as a student during the entire internship period. Formal documentation of which may be requested at any time. Students from outside the EU need to fill in an NUFFIC agreement, which needs to be signed by the student and the university.
Please note that the contents of our regular internship assignments are not suitable for professionals (and/or MBA students) with professional work experience.