Thesis in Multi-Modal Predictions for End-to-End Architectures

In the context of autonomous driving, the need for accurate predictions is paramount. Achieving more robust predictions is crucial for ensuring the safety and efficiency of autonomous vehicles. By accurately predicting the future movements of surrounding objects and other vehicles, autonomous driving systems can proactively plan and adapt their actions, thereby reducing the risk of accidents and improving overall driving experience. Additionally, the ability to provide feedback from predictions back to perception allows the system to continuously learn and improve its overall end-to-end capabilities, leading to enhanced decision-making in dynamic environments. Education: studies in the field of Computer Science or comparable Experience and Knowledge: in data analysis and visualization; proficiency in Python and tools like Pandas, NumPy, and Matplotlib; knowledge of deep learning frameworks; familiarity with TensorFlow, Keras, or PyTorch; understanding of computer vision; experience with OpenCV or similar libraries; familiarity with autonomous systems (e.g. automated driving) Personality and Working Practice: you are eager to learn and able to tackle complex challenges, develop innovative solutions, clearly articulate technical concepts to both technical and non-technical audiences Languages: fluent in English