Summer Research Intern

Descripción del puesto:

Bell Labs is a worldwide research and development community that focuses its efforts on key technologies for telecommunications. It is internationally renowned as the birthplace of modern information theory, the transistor, the laser and the UNIX operating system. Nowadays we are working on the network of 2020. In this new scenario of interconnected things, people will, almost more than ever before, matter. The Social & Responsible AI team answer questions related to ethical considerations, societal impacts, and responsible practices in the development and deployment of AI. The team provides a vibrant multidisciplinary research environment with close links to top academics.
Number of Positions: 5
Duration: 3 months
Date: June to August, 2026

* Collaborate with our Cambridge Researchers and with worldwide colleagues, initiate novel ideas, and develop them into algorithms and applications.
* Leverage big data to discover patterns and solve strategic business problems using structured and unstructured data sets across multiple environments.
* Push your contribution by 1) working closely with Nokia's business units; and 2) publishing at top-tier conferences such as ACL, NeurIPS, CoNLL, EMNLP, NACCL, EACL, ICML, ICWSM, WWW

Requerimientos del candidato/a:

* Enrolled in a PhD Degree in Computer Science, Machine Learning, Computational Linguistics, Natural Language Processing, Applied Mathematics, or a related field.
* Proven NLP experience, including topic clustering, text classification and sentiment analysis, and experience with other unstructured data problems.
* Experience with recurrent neural networks and word embedding techniques such as word2vec is beneficial.Experience of building ML and NLP models in Deep Learning frameworks in Python.
* Preferred: Experience with recurrent neural networks and word embedding techniques; Expertise in either: fact extraction, entity linking, question answering, semantic parsing, or natural language generation; Skills in visualizing/presenting data using BI tools