Machine Learning in the Wild. Exploring new Applications using Deep Neural Networks
AI has advanced over the last decade tremendously. Applications range from the detection of cats in YouTube videos (2012) to more serious research as skin cancer classification .
“Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.”  Therefore a vast range of possibilities for the use of deep learning is present and should be explored.
You will be supervised by one or more of four PhD-Candidates actively working with Machine Learning technologies at ITIV. Possible project subjects will be discussed in person. Own project ideas are also welcome!
- Data Acquisition/Preprocessing
- Network Architecture Design
- Network Training
- Creation of Prototypes/Sample Applications
- Highly Motivated
- Ambition to tackle difficult problems
- Experience in Machine Learning and/or Programming is a plus
 A. Esteva et al. “Dermatologist-level classification of skin cancer with deep neural networks”, Nature, vol. 542, pp. 115-118, 2017.  Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning”, Nature, vol. 521, no. 7553, pp. 436–444, 2015.