Sort by
Refine Your Search
-
Category
-
Employer
-
Field
-
machine learning. We particularly value depth of knowledge, originality, and the potential for cross-disciplinary innovation. Relevant application areas may include (but are not limited to) natural
-
of the project is to exploit such data to develop generative models for aptamer design. The candidate is expected to have a strong background in machine learning and statistical physics, with a real interest for
-
modeling, meta-modeling, or statistical learning Strong programming skills in Python and/or C++ Familiarity with EDA tools, digital architecture, or embedded systems is a plus. In accordance with
-
rank models [Sportisse et al., 2020], random forests [Stekhoven and Buhlmann, 2012] or deep learning techniques with variational autoencoders [Mattei and Frellsen, 2019, Ipsen et al., 2021]. One
-
are particularly interested in candidates who combine computational biology, data science, and machine learning/AI with deep biological insight. While wet lab activities are welcome, they are not mandatory. However
-
detection, scene graph generation. A prospective method is transfer learning which freezes the backbone of the LiDAR-VLM model and fine tunes its output layers to adapt it to a particular task. An important
-
interactions If you meet the required education and experience but don't tick every technical skill listed, we still encourage you to apply! We value motivation, and willingness to learn, and a passion for data