Contribute your computer vision and software skills to engineer the next generation of cell lines for cost-effective, scalable and delicious cruelty-free meat!
'Cultivated meat', the production of genuine meat products from in vitro culture of animal cells, is an emerging biotechnology within the field of cellular agriculture with the potential to revolutionise food production from both an environmental and ethical perspective. Unfortunately, higher eukaryotic cells have severe limitations when cultured in vitro, including limited cellular lifespans, slow proliferation rates and demanding medium requirements. Characterizing cell properties and identifying cell lines with promising phenotypes typically require months-long, expensive characterization experiments.
Modern computer vision pipelines can significantly speed up this characterization process! We are hiring a postdoctoral scientists to join the group for a fully-funded project of 2 years. Within the Computer Vision Lab at TU Delft, you will build deep-learning based vision pipelines to preprocess, segment cells and perform phenotype classification and regression on a variety of microscopy images. You will deal with images from multiple microscopy domains, in order to predict important cell parameters to determine the viability of cell lines. You will deal with curating large amounts of data obtained from novel cell lines by the Flack group, and collaborate with microscopy scientists on data aquisition questions.
We're looking for someone with excellent software development and object-oriented programming skills, ideally with some experience in biomedical image processing.
Within the scope of machine learning and computer vision, there will be freedom to suggest your own research directions, and to become acquainted with new techniques and approaches. There will be ample opportunities to supervise PhD, MSc and BSc students in the group, and to become familiar with grant writing and funding acquisition. Career advice and discussion will be available for both academic and industry-minded scientists. We are a friendly, dynamic and diverse group, performing research on a wide-range of computer vision and machine learning topics. We are looking for highly motivated candidates with strong project management and communication skills who's excited to grow with us in this interdisciplinary project.
Job requirements
We are looking to build a diverse and complementary project team. Applicants should have or expect to receive a PhD in Computer Science, Engineering or a related field. Experience in some or all of the following or related fields are preferred: biomedical image processing, image or signal processing, software development, machine learning, and pattern recognition.
The successful applicant will have:
- very strong programming skills and experience with deep learning libraries;
- strong python and object-oriented programming skills;
- experience in developing and documenting large software pipelines, including some familiarity with frameworks such as docker and weights and biases;
- extensive experience with computer vision and managing large datasets;
- curiosity and critical thinking skills;
- the ability to work in a multi-disciplinary team;
- motivation to meet deadlines;
- curiosity or willingness to learn about the relevant biology and microscopy rearch;
- good oral and written communication skills;
- an interest in communicating their research results to a wider audience;
- an aptitude for guiding students;
- proficiency in English;
TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.
At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.
Challenge. Change. Impact!
Faculty Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment - which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.
to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.
Additional information
Are you interested in this vacancy? Please apply no later than 11 January 2026 via the application button and upload the following documents:
- CV
- Motivational letter
Please note:
- You can apply online. We will not process applications sent by email and/or post.
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