40 phd-studenship-in-computer-vision-and-machine-learning PhD positions at Nature Careers in Germany
Sort by
Refine Your Search
-
or infrastructure. This is what makes our daily work so meaningful and exciting. The Division of Computational Genomics and Systems Genetics is seeking from October 2025 a PhD Student in Deep Learning for Rare
-
academic area such as applied mathematics, computer science, physics, biomedical or electrical engineering or similar disciplines. Good programming expertise (Matlab, C++, Python or equivalent) and
-
/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
-
Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
-
through the DKFZ International PhD Program and DKFZ Career Service Our Corporate Health Management Program offers a holistic approach to your well-being Contact: Prof. Dr. Dieter Saur Telefon: +49 89 4140
-
. The research program may also involve a numerical simulation component. Your tasks #analyzing measurements of ocean turbulence using autonomous glider vehicles #use and develop machine learning methods
-
Columbia (UBC) in Vancouver offers 4 PhD Positions The program comprises a series of complementary experimental and theoretical projects, investigating and controlling the electron and nuclear dynamics down
-
, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
-
social challenges of Advanced Air Mobility (AAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly
-
schemes a secure job flexible working hours and childcare support the possibility of mobile working an idyllic green campus, which is easily accessible by bicycle, public transport or car free use