Напиши текст вакансии для должности "Research Scientist / Research Engineer (machine learning)". Название компании: "The Skolkovo Institute of Science and Technology". Расположение: Москва. График работы: Полный день. Опыт работы: Нет опыта. Ключевые навыки: 'Python', 'PyTorch', 'Machine Learning', 'Английский язык', 'Numpy'

About the job We are looking for a research scientist to develop next-generation technologies in the challenging field of Machine learning. In particular, we plan to advance similarity learning and sequential data processing and their applications. As a research scientist, you will work on diverse projects in professor Zaytsev&#39;s research group. We need the research scientist to be versatile, display leadership qualities and be enthusiastic about taking on new and challenging machine learning problems. You will work in a team, that regularly publishes at top machine learning venues and develops projects for major companies. Prospective research directions: For this position, you&#39;ll start to work on the project related to similarity learning for sequential data that come from the Oil&amp;Gas industry. Later, we expect that you can carry on independent research or continue your endeavor in this direction.   Responsibilities  Develop machine learning algorithms. Automate and run machine learning experiments. Provide consistent solutions for real world problems. Prepare papers for scientific conferences. Help with data acquisition and preprocessing.      Minimum qualifications:  MS degree in Computer Science. 2 years of work in Machine Learning. Experience with Python (PyTorch, Jupyter Notebook as a plus) Good spoken and written English.      Preferred qualifications:  PhD degree in Machine Learning or related technical field. Experience with deep learning: pytorch or similar. 2+ Scopus/WoS publications.        Related publications 1. Fursov, Ivan, et al. &quot;Adversarial Attacks on Deep Models for Financial Transaction Records.&quot; SIGKDD (2021). 2. Kail, Roman, Evgeny Burnaev, and Alexey Zaytsev. &quot;Recurrent convolutional neural networks help to predict locations of earthquakes.&quot; IEEE Geoscience and Remote Sensing Letters (2021). 3. Romanenkova, Evgeniya, et al. &quot;Real-time data-driven detection of the rock-type alteration during a directional drilling.&quot; IEEE Geoscience and Remote Sensing Letters 17.11 (2019): 1861-1865. 4. Klyuchnikov, Nikita, et al. &quot;Data-driven model for the identification of the rock type at a drilling bit.&quot; Journal of Petroleum Science and Engineering 178 (2019): 506-516. 5. Fursov, Ivan, et al. &quot;Sequence embeddings help to identify fraudulent cases in healthcare insurance.&quot; arXiv preprint arXiv:1910.03072 (2019).