AI Research Scientist

Job Description Job description: What you can look forward to as AI Research Scientist (m/f/d): Conduct cutting-edge research to identify and develop novel AI/ML methodologies, including Deep Learning (DL) and Reinforcement Learning (RL). Design and conduct experiments to test hypotheses, validate new approaches, and compare the effectiveness of different ML algorithms. Analyze data to uncover hidden patterns and relationships that can inform the development of new AI techniques. Stay at the forefront of the field by keeping abreast of the latest advancements in algorithms, tools, and theoretical frameworks. This might involve researching areas like interpretability of machine learning models or efficient training methods for deep neural networks. Prototype and explore the potential of advanced machine learning models, including deep learning architectures like convolutional neural networks (CNNs) or recurrent neural networks (RNNs). Contribute to the development of fundamental algorithms and frameworks that can be applied to various machine learning problems. This may involve improving existing algorithms or exploring entirely new approaches. Focus on theoretical aspects of model design, such as improving model efficiency, reducing bias, or achieving explainability in complex models. Document research methodologies, experimental procedures, and theoretical contributions for future reference and knowledge sharing within the research community. Contribute to the development of the research team's long-term research goals and strategies. Your profile as AI Research Scientist (m/f/d): Master's degree in Mathematics, Computer Science or other related technical fields. Phd is good to have. 8+ years of experience in research development, with a strong understanding of data structures and algorithms. 5+ years of experience building and deploying machine learning models in production. Proven experience with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn. Experience with distributed computing systems and large-scale data processing & Excellent communication and collaboration skills. Contribution to invention disclosure process & A strong publication record in top machine learning conferences or journals. Profile description: What you can look forward to as AI Research Scientist (m/f/d): Conduct cutting-edge research to identify and develop novel AI/ML methodologies, including Deep Learning (DL) and Reinforcement Learning (RL). Design and conduct experiments to test hypotheses, validate new approaches, and compare the effectiveness of different ML algorithms. Analyze data to uncover hidden patterns and relationships that can inform the development of new AI techniques. Stay at the forefront of the field by keeping abreast of the latest advancements in algorithms, tools, and theoretical frameworks. This might involve researching areas like interpretability of machine learning models or efficient training methods for deep neural networks. Prototype and explore the potential of advanced machine learning models, including deep learning architectures like convolutional neural networks (CNNs) or recurrent neural networks (RNNs). Contribute to the development of fundamental algorithms and frameworks that can be applied to various machine learning problems. This may involve improving existing algorithms or exploring entirely new approaches. Focus on theoretical aspects of model design, such as improving model efficiency, reducing bias, or achieving explainability in complex models. Document research methodologies, experimental procedures, and theoretical contributions for future reference and knowledge sharing within the research community. Contribute to the development of the research team's long-term research goals and strategies. Your profile as AI Research Scientist (m/f/d): Master's degree in Mathematics, Computer Science or other related technical fields. Phd is good to have. 8+ years of experience in research development, with a strong understanding of data structures and algorithms. 5+ years of experience building and deploying machine learning models in production. Proven experience with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn. Experience with distributed computing systems and large-scale data processing & Excellent communication and collaboration skills. Contribution to invention disclosure process & A strong publication record in top machine learning conferences or journals.