Вакансия № 15477114 на должность "Machine Learning Research Fellow/ Intern" в городе (регионе) Минск, Беларусь от компании "Новикова Анна Анатольевна" на сайте Электронный Центр Занятости Населения
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Обязательное требование к опыту работы искомого сотрудника: более 6 лет.
График работы: удаленная работа.
Тип занятости: стажировка.
Вакансия № 15477114 добавлена в базу данных: Четверг, 11 апреля 2024 года.
Дата обновления этого объявления: Четверг, 25 апреля 2024 года.
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Работодатель может предложить примерно следующую оплату труда: от 4000 USD на вакантной должности "Machine Learning Research Fellow/ Intern".
Interested in working on hard problems at scale?
We are looking for current and graduating MSc/PhD students who would like to work with us on a research project. You should have some prior practical industry experience, strong coding and software engineering skills, as well as a publications in relevant CV/ML conferences or journals.
IMI builds ML products and services used by some of the largest companies in the world. Come learn from our veteran team of machine learning and distributed systems experts, hailing from Stanford, MIT, Apple, and Cloudera.
We are now opening applications for our 2020 ML Fellows/Interns program. You will be expected to work remotely: we have a large distributed workforce working alongside the teams in our San Francisco, Berlin, or Helsinki centers, and we are highly comfortable with remote collaboration.
We conduct original research in areas like unsupervised and active learning, motivated by the unique problems and datasets we have access to as one of the larger users of cloud resources for ML.
You will have the opportunity to publish with our scientists if your work yields results, and we have a strong commitment to open source code and (when possible) datasets.
Research interests:
A background in representation learning, theoretical computer vision, or statistical physics will be helpful, as well as applied applications of Deep Learning to Computer Vision problems.
Current areas of interest include Active Learning, Self- and Semi-supervised learning, Uncertainty Estimation (Bayesian Neural Networks), Anomaly detection, Deep Hashing for Image Retrieval, Weak Supervision, Zero-shot learning, Domain Adaptation, Meta-Learning.
Functional skills:
We work primarily in Python for research, with pyTorch and Tensorflow being our preferred tools. You will have access to our in-house distributed training and inference infrastructure, which has been designed for ease of use but still benefits from basic knowledge of distributed systems.
Experience with cloud-based training and dealing with preprocessing huge datasets is beneficial.
We also strive for a high degree of programming competence within our research group, as we have found that good discipline in implementing ideas makes your task easier and collaboration more pleasant. If you are one of the many excellent researchers who have never written a unit test this will likely change.
Please send your CV, github, and a brief description of what most interests you, along with your dates of availability and preferred location.
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