Thermo Fisher Scientific
Thermo Fisher Scientific
Thermo Fisher Scientific hayot fanlari, sog‘liqni saqlash va analitik kimyo sohalarida faoliyat yurituvchi tashkilotlar uchun ilmiy asboblar, reagentlar, sarf materiallari va dasturiy ta’minot xizmatlarini ishlab chiqadi hamda yetkazib beradi. Uning mahsulotlar portfeli diagnostika, laboratoriya ish jarayonlarini avtomatlashtirish va dori vositalarini kashf etish kabi yo‘nalishlarni qamrab olib, laboratoriya tadqiqotlari va ishlab chiqarishni qo‘llab-quvvatlaydi. Kompaniyaning mahsulot va xizmatlari tadqiqot hamda sog‘liqni saqlash jamoalariga murakkab laboratoriya jarayonlarini boshqarish va ilmiy rivojlanishni ilgari surishda yordam berish uchun qo‘llanadi.

Physical AI Research Intern (Remote, United States)

Research and develop physical AI systems spanning computer vision, vision-language models, 3D perception, and robotics. Turn experimental work into reproducible code, deployable services, evaluation reports, and demonstrations.

Tavsif

  • Develop and fine-tune models for detection, tracking, segmentation, pose and activity recognition, and scene understanding
  • Train and assess vision-language models for grounding, dense captioning, temporal question answering, and tool use
  • Create retrieval-augmented and agent-based workflows for perception and action
  • Prototype perception-driven control policies with simulated and real-world data
  • Connect perception systems to planners and task graphs for manipulation, navigation, and safety applications
  • Prepare datasets, define evaluation methods and KPIs, and conduct ablation studies
  • Deploy research systems as dependable services with Kubernetes, Docker, Ray, and FastAPI
  • Implement profiling, telemetry, and continuous integration to support reproducible research
  • Coordinate multi-agent pipelines across perception, reasoning, simulation, and code generation
  • Investigate video understanding, 3D scene grounding, edge inference, multimodal assessment, and sim-to-real policy performance
  • Produce publishable or open-source research, production-ready components, reproducible implementations, evaluation reports, and demos

Talablar

  • Pursuing a master’s or Ph.D. in computer science, electrical engineering, robotics, or a related field, with active publications in computer vision, machine learning, or robotics
  • Advanced Python and PyTorch or JAX skills, including familiarity with CUDA profiling and mixed-precision training
  • Research experience in computer vision plus at least one area such as vision-language models, embodied or physical AI, or 3D perception
  • Ability to progress from published research to implementation, ablation studies, and rigorously tracked results
  • Experience with video models, diffusion, 3D Gaussian Splatting or NeRF, SLAM, or scene reconstruction
  • Previous work involving multimodal grounding or temporal reasoning
  • Familiarity with ROS2, DeepStream or TAO, or edge-inference optimization using TensorRT or ONNX
  • Experience with scalable training using Ray, distributed data loaders, or sharded checkpoints
  • Strong software engineering practices, including testing, linting, profiling, containerization, and reproducibility
  • Public technical work such as GitHub projects, first-author publications, or substantial open-source contributions

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