Internship / Post M.Sc. / Postdoc at AI Research Team Full-time

Company Description

Do you care about impact on people? Do you want to publish your work in top tier-1 conferences?

Bosch Center for Artificial Intelligence in Israel is a unique place that researches and implements cutting edge technologies for manufacturing, automotive, robotics, and industrial applications. We are looking for Ph.D students / Ms.C students / Doctors in the areas of:

• Reinforcement Learning
• Robotics (Manipulation)
• Computer Vision (FMs, Theory, Applications)
• Large Language Model
• Planning, and Control & Machine Learning

If such things interest you, then Bosch Israel is looking forward for your application!

Job Description

• Duration: 5-12 months
• Create something new: As a research intern scientist you will conduct excellent research on real-world project. The results will be published at the leading AI venues and transferred for implementation in Bosch products.
• Strong SW engineering capabilities: High proficiency in Python, Deep Learning frameworks, and CV methods in general.

Qualifications

• Currently pursuing (or completed) a PhD or MsC in CS / EE / IE / ME or related faculties
• Pursuing a thesis in one of the following areas:
  • Reinforcement Learning
  • Computer Vision
  • Autonomous Vehicles
  • Robotics
• Self-confident and responsible team player with excellent communication skills
• Initiative, highly motivated and motivating character for the team and partners
• Foreign applicants should arrange a work permit for Israel independently

* Applicants should be eligible to work in Israel or be entitled to receive a work permit!

Additional Information

APLLY NOW!!!

Bosch AI site: https://www.bosch-ai.com/

Haifa team site: https://dotd.github.io/

Selected papers:

• L Cohen, Y Mansour, M Moshkovitz. Finding Safe Zones of Markov Decision Processes Policies. NeurIPS 2023.
• E Kosman, D Di Castro. GraphVid: It only Takes a Few Nodes to Understand a Video. ECCV 2022
• Y Miron, C Ross, Y Goldfracht, C Tessler, D Di Castro. Towards autonomous grading in the real world. 2022
• S Di-Castro, S Mannor, D Di Castro. Analysis of stochastic processes through replay buffers. ICML 2022
• V Tchuiev, Y Miron, D Di Castro. DUQIM-Net: Probabilistic Object Hierarchy Representation for Multi-View Manipulation, IROS 2022.
• S. Di Castro Shashua, S. Mannor, D. Di Castro. “Sim and Real: Better Together”. NeurIPS 2021.
• J. Oren, C. Ross, M. Lefarov, F. Richter, A. Taitler, Z. Feldman, D. Di Castro, C. Daniel. “SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems”. SOCS, 2021.
• O. Spector, D. Di Castro. “InsertionNet – A Scalable Solution for Insertion”. IROS 2021, RAL.
• E. Kosman, D. Di Castro. Vision-Guided Forecasting–Visual Context for Multi-Horizon Time Series Forecasting. arXiv preprint arXiv:2107.12674, 2021. Presentation
• Botach, Y. Feldman, Y. Miron, Y. Shapiro, D. Di Castro. BIDCD-Bosch Industrial Depth Completion Dataset. arXiv preprint arXiv:2108.04706

Videos To Watch
https://www.youtube.com/watch?v=7Fm5MUaaxEY