R&D Master internship
Powder is the TikTok of gaming. Through specialized tools, we let gamers import their gameplay from PC, consoles (Xbox, PS4, Switch) or mobile. We let them edit it: trimming, visual + sound effects, overlays, zoom in/out, fast/slow motion, etc. And we let them share it, to our community or outside the app: Snap, TikTok, Instagram… Share clips. Make friends. Play with them. Repeat.
Powder is a deep tech startup. Our AI detects highlights from hours of gameplay, to help users trim and edit clips. It’s the engine behind our feed. Our team comprises four AI professionals, and we have several patents ranging from in-game highlights detection to emotions detection in the context of a mobile platform.
Powder is booming – fast. Since December, we’ve created a 100k community on Discord and Instagram. 150+ people volunteer on the project. Our open beta lasted only 2 weeks – that’s the time it took to reach TestFlight’s 10k users limit. We’ve reached 300 monthly active users in just 4 months. And we’re just getting started.
Powder has raised $3.5m+ from leading Silicon Valley investors who have invested at early stages in Stripe, Airbnb or Snapchat. Members of our 15-people strong team have previously worked with Zenly, GoPro, Lydia, Mindie, PandaScore, MolotovTV, Artomatix, Nike and BCG, and have raised over $10m and sold multiple companies in the past.
The Laboratory of Images, Signals and Intelligent Systems (LISSI) belongs to the University Paris-Est Créteil (UPEC). The LISSI develops multidisciplinary, theoretical and applied research activities in the field of Information and Communication Sciences and Technologies and in particular in artificial intelligence. The targeted applications are mainly in the field of healthcare and wellbeing technologies and are focused on challenging issues such as support for medical diagnosis and therapeutic monitoring, assistance to elderly and paretic patients, and e-health.
OUR INTERNSHIP PROGRAM/Tasks
We are seeking bright and highly motivated master students in France, who can work in the field of artificial intelligence. The project will develop innovative deep learning approaches for behavior recognition from videos. A multi-modal approach will be proposed using speech, facial images and text. More details about the project will be given during the interview for confidentiality reasons.
The selected candidate will have the chance to work in an interdisciplinary team and a big consortium of data scientists. This internship can lead to a permanent contract or PhD scholarship.
The candidate must be an M2 Master student or in 5th year of an engineering school in France.
Has done M1 in computer science, applied mathematics or electrical engineering, with a focus on machine learning.
Experience in Deep learning and data analysis.
Experience in the signal and image processing.
Demonstrated record of high-performance programming skills in python.
Demonstrated analytical, verbal, and scientific writing skills in English.
Internship duration will be 6 months starting from January 2021 at an early date to start. The latest date to start the internship will be March 2021.
Please send your CV + transcripts + cover letter + recommendation letters to Alice.firstname.lastname@example.org (before November 15, 2020).
 Mabrouk, A. B., & Zagrouba, E. (2018). Abnormal behavior recognition for intelligent video surveillance systems: A review. Expert Systems with Applications, 91, 480-491.
 Kirsh, S. J., & Mounts, J. R. (2007). Violent video game play impacts facial emotion recognition. Aggressive Behavior: Official Journal of the International Society for Research on Aggression, 33(4), 353-358.
 Zhu, G., Huang, Q., Xu, C., Xing, L., Gao, W., & Yao, H. (2007). Human behavior analysis for highlight ranking in broadcast racket sports video. IEEE Transactions on Multimedia, 9(6), 1167-1182.
 Sainath, T. N., Vinyals, O., Senior, A., & Sak, H. (2015, April). Convolutional, long short-term
memory, fully connected deep neural networks. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4580-4584). IEEE.
Baltrušaitis, T., Chaitanya, A., Louis-Philippe, M., 2018. Multimodal machine learning: A survey and taxonomy. IEEE transactions on pattern analysis and machine intelligence 41, 423–443
(c) GdR 720 ISIS - CNRS - 2011-2020.