Nagi’s technology employs robotics and AI for the automated culture and analysis of micro-organisms within microfluidic chips, as powerful biological models to investigate human diseases and test their potential cures. In this framework, we are currently looking for a highly motivated intern to join our team and actively improve the company’s core computer vision algorithms.
Main tasks and responsibilities:
The intern will directly work with Nagi’s software and data science team on the following tasks:
- Improvement of the performance of our instance segmentation algorithm for the detection of worms and other objects in microscope images.
- Exploring various techniques, such as data augmentation, and leveraging unlabeled data by using self-supervised and unsupervised methods.
- Exploring state-of-the-art computer vision methods and building new models that could be integrated into our data analysis pipeline.
Skills and experience required:
- Knowledge of one or several programming languages (preferred Python)
- Experience with machine learning algorithms and tools (e.g., PyTorch, TensorFlow), artificial intelligence, deep learning
- Excellent presentation and communications skills
- Analytic, structured, highly driven, and solution-oriented person
- Experience in image processing and/or computer vision
- Motivated to read scientific literature and keep up with the state-of-the-art
- Interest in engineering applications in life sciences
What we offer:
- A very dynamic, multidisciplinary, and international team of highly motivated people.
- A modern working environment based at the EPFL Innovation Park in Lausanne, Switzerland.
- The possibility to significantly contribute to shaping Nagi Bioscience’s future.
- An excellent working experience and the possibility of a future career in a Swiss deep-tech startup with high potential and a clear vision.
Start date: as soon as possible
Type of contract: internship
Activity rate: 100%
Duration of contract: preferably 4-6 months
Applicants should submit a cover letter and a detailed CV in PDF format