Scientific Internship: Machine learning for studying the timing of development in a nematode worm

Date posted May 10, 2022
Type Scientific Internships

How timing of development is controlled remains one of biology’s most enduring mysteries. For instance, how does the human body know to enter puberty more than ten years after its birth? To study developmental timing, our group has recently developed a microscopy approach to make time-lapse movies of the dynamics of cells in time, inside freely moving and growing nematode worms as they develop from hatchlings to adults. Such movies make it possible for the first time to systematically uncover errors in timing of cellular events, such as cell divisions, in mutants known to impact developmental timing. However, a key challenge is extracting the timing of cellular events from these time-lapse movies.

Goal of the project

You will adapt cell-tracking techniques, based on convolutional neural networks, that we previously developed in our group, to automatically track the movements and divisions of cells inside the growing and deforming bodies of individual worms. You will use this approach to measure the timing of cell divisions of different cell types in the worm and study how this is changed in mutants that are known to perturb timing on the level of the whole animal.

About the group

The ‘Quantitative Developmental Biology’ research group uses a quantitative, physics-inspired approach to study problems in developmental biology, focusing both on the small roundworm C. elegans and intestinal organoids. The aim of the research is to elucidate how living organisms reliably build their bodies, maintain their tissues or respond to their environment despite the considerable underlying variability on the molecular level.


You have a Bachelor’s degree in physics, chemistry or biology and will participate in a Master study during the entire internship period. The internship must be a mandatory part of your curriculum. Experience with Python or another programming language is a pre, but experience with machine learning is not required. You have a nationality of an EU member state and/or you are a student at a Netherlands University. Please note: As of January 2021 the UK is no longer an EU member state. You must be available for at least 3 months.

Terms of employment

At the start of the traineeship your trainee plan will be set out, in consultation with your AMOLF supervisor.

Contact info

Dr. J.S. van Zon
Group leader Quantitative Developmental Biology
Phone: +31 (0)20-754 7100

You can respond to this vacancy online via the button below.
Please annex your:
–  Resume;
–  List of followed courses plus grades.

Applications will be evaluated on a rolling basis and as soon as an excellent match is made, the position will be filled.

Online screening may be part of the selection.

AMOLF is highly committed to an inclusive and diverse work environment. Hence, we greatly encourage candidates from any personal background and perspective to apply.

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