Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
Your Job: Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch
-
degree (or equivalent) in Data Science, Computational Biology, Bioinformatics, Computer Science, Physics or a related field Solid programming skills and knowledge in deep learning, statistical modelling
-
in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning, particularly deep learning and optimization methods Excellent
-
learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
-
(TV-L Brandenburg). Background: Addressing climate change and biodiversity loss requires a deep understanding of global land-use dynamics and the economic trade-offs involved. We aim to develop and
-
-driven to take a deep dive into the unknown. You’re extremely capable, using creativity and ingenuity to rise to new challenges. You’ve got an excellent M.Sc. degree in cancer genetics, molecular biology
-
: Building interpretable causal models to explain patterns (e.g., congestion dynamics), enabling transparency in high-stakes decision-making. We combine statistical data mining, deep learning, and domain
-
microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
-
/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
-
generating a high-quality training dataset to support the development of the AI foundation model Contributing to the design and implementation of advanced deep learning architectures (e.g., Transformers, CNNs