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Young Medical Delta


Exhibition | Speeddate | Workshop

Number of employees



LUMC Leiden, TU Delft, Erasmus MC


The Netherlands

Company profile

Joining forces to innovate tomorrow’s health care

YOUNG Medical Delta provides a platform for students, young researchers and young professionals who are at the start of their career in life sciences, health, technology or even a combination of those. YOUNG Medical Delta consists of a network of study associations from the LUMC Leiden, TU Delft and Erasmus MC Rotterdam, all focused on topics in the sectors of life sciences, health and technology. Through Medical Delta it also has connections with different companies, and through its connections YOUNG can offer different opportunities to their members. By exchanging ideas and opportunities in the field of medical technology, YOUNG Medical Delta can support and facilitate activities of the study associations. Our platform can help the associations with the promotion of events and activities, as well as assist in connecting them, and students, to interesting speakers and connections. Together with the network of Medical Delta, YOUNG Medical Delta has been working on a database filled with internships and thesis projects, to help students find interesting opportunities in the field of medical technology. Recently, Medical Delta started 12 interesting new scientific programmes. Excited to learn more? Visit our stand!

Relevant studies

  • Chemistry
  • Computer Sciences
  • Bio-Pharmacy
  • Biology
  • Health
  • Life Sciences

Study levels

  • HBO
  • WO
  • PhD

Student backgrounds

  • Dutch
  • International


Masterproject: Dealing with missing scans in a radiomics context

Erasmus MC Rotterdam.
The World Health Organization (WHO) currently grades brain tumors into
four grades. It has recently become clear that it also is important to take
into account the genetic mutations of these tumors, maybe even more so than
the current classification based on the WHO grade. Tumors which have the
same WHO grade but a different genetic mutation may develop at different
rates, and respond differently to radiotherapy and chemotherapy.

Therefore it is important to know the genetic profile of a tumor, so that it
can be included in the decision making. However, the determination of these
genetic mutations currently still requires a biopsy and it can not be done non-
invasively. It would be beneficial to the patient if a biopsy was not required.
Therefore there has been a lot of interest in the field of radiogenomics, where
one tries to predict tumor characteristics based on quantitative features ex-
tracted from imaging data.

This means that imaging data is required for a patient from which relevant features can be
extracted. However, in obtaining this imaging data one is limited to the clinical practice.
The quality of this data is often highly variable, and not all different types of scans might be
present for all patients. Therefore in this project we would like to look at a method which
can generate the missing scans from the scans that are present for a patient. These scans
can then be used in the radiogenomics pipeline, creating a larger database of patients that
can be included as well as making sure that all patients can be evaluated in the pipeline.

Physics and Astronomy Chemistry Biology

Medical Technology Intern

NLC | The Healthtech Venture Builder

Are you a technical or medical student ready to (temporarily) shed your lab coat or put away your stethoscope? We offer interns the opportunity to make real impact, both to patients’ lives as well as your career!

What we do:

NLC turns research into products and medical innovations into sustainable companies. We have a vibrant ecosystem that brings together the right people from medical, corporate, and entrepreneurial settings to build the future of healthcare. Our diverse team consists of a highly skilled and knowledgeable partner group and a growing pool of (junior) business developers.

What you will do:

As an intern you will work to find, select, and validate high potential innovations with the NLC business developers. You will work on developing business plans, financial models and investment plans, identifying stakeholders and target groups. We talk to buyers and end users, IP lawyers, medical specialists, CE experts, subsidy advisors; do market research, competitor analyses, visit conferences, make presentations, and much more!

Sounds complex? It can be. Luckily our team of venture building professionals enjoys sharing their knowledge with you and taking up challenges together!

What we look for:

We are looking for motivated students who want to experience entrepreneurship, and use their expert knowledge to help find and select Europe’s most promising inventions.

High potential students in the last phase of their academic study in the field of medicine, (biomedical) engineering, or related;
Strong, professional communicators in both Dutch and English with an eye for detail;
Adaptive capabilities in a constantly changing environment;
Batteries-included team players who are more interested in making history than repeating it;
Affinity with innovation, entrepreneurship, technology, healthcare, and/or startups.

We offer:

A full time internship of 4-6 months which can be altered to your personal interests and learning goals;
An inspiring learning-by-doing environment;
The chance to contribute to realising innovations with a great impact on healthcare;
The opportunity to become part of the NLC network; a large, valuable and diverse network ranging from a 1000+ medical doctors and brilliant inventors to Corporate Partners like Philips;
An internship compensation of 300-500 euro per month.
Last but not least: a tasty and healthy lunch every work day!

Chemistry Bio-Pharmacy Biology Health Life Sciences

Master student: Microfluidic 3D sarcoma model for drug screening

We are looking for a motivated MSc student to create a microfluidics platform in Eindhoven which they then would combine with a 3D cell culture model of sarcoma developed in Leiden. Microfluidics will be used to enhance the representability of the model in prediction of patient treatment response. The MSc student would be jointly supervised by Prof. Jaap den Toonder (Microsystems group, TU/e) and Prof. Judith Bovee (department of Pathology, LUMC), both experts in their fields. Some travel would be involved. Initially most of the work would entail design and production of the microfluidic chip in Eindhoven and later culture of sarcoma within the chip in Leiden.

Chemistry Bio-Pharmacy Biology Health Life Sciences

Master Project: Deep learning method for automated bone age inference

Erasmus MC.

Job Description
Bone age assessment is frequently performed in pediatric patients to evaluate growth
and to diagnose a multitude of endocrine disorders and pediatric syndromes. For
decades the determination of bone maturity has relied on a visual evaluation of the
skeletal development of the hand and wrist, most commonly using the Greulich and
Pyle atlas. Deep learning methods provide a unique opportunity to develop pipeline that
automatically extract the key morphological features of ossification in the bones to
provide a more effective and objective approach to skeletal maturity assessments.
During this graduated project (6-9 months) the student will work on deep learning
algorithm for automated bone age prediction based on thousands of hands Dual-energy
X-ray absorptiometry (DXA) images of children from Generation R cohort (population-
based prospective cohort study from fetal life until adulthood:

Work environment
Erasmus MC is an internationally recognized center for high-quality, compassionate
care, highly rated transfer of knowledge and high-quality knowledge development in the
fields of illness and health. The master project will be conducted in collaboration
between two departments: Department of Oral and Maxillofacial Surgery, Special
Dental Care and Orthodontics and Department of Medical Informatics. Our
departments are known for its forefront research and its state-of-the-art facilities.

We are looking for a creative MSc student interested in pattern recognition, deep
learning with relevant background, to be able understand the basic principles of neural
networks, write program (with python) as well as to learn how to assess skeletal (bone)
age. Good communication and writing skills in English are necessary.

Chemistry Bio-Pharmacy Biology Health Life Sciences