18 Student English-speaking jobs in Grenoble

This 12-month Energy Tech Accelerator Graduate Program offers two six-month missions for early-career engineers. Candidates apply to apply AI and emerging technologies to challenges in areas like Sustainability, Software & Services, and Industrial Automation.

Student Researcher role involves participating in exploratory research projects to develop solutions for real-world, large-scale problems. Candidates must be pursuing a degree in relevant fields and have experience in a computer science area.

This 12-month Energy Tech Accelerator Graduate Program offers two six-month missions to apply AI and emerging technologies. Candidates should have a Master's degree, exposure to AI, and the right to work in France.

Research and development internship in Embedded AI focusing on Physical AI for industry. Tasks include designing AI pipelines, developing simulation environments, and evaluating multimodal models for industrial applications. Requires an engineering or Master 2 degree in Embedded Systems AI or Robotics.

Contribute to industrial automation projects by contributing to product specifications, compiling resources, and developing BIM objects. Requires technical degree, Python proficiency, and English skills.

Research Internship focusing on Network Traffic Compression for sustainable networks. Review compression mechanisms, build benchmarks for ratio, throughput, memory, and latency, and share results with IETF/3GPP.

Internship focused on improving 3D infant motion estimation from RGB video. Tasks include benchmarking HMR methods, creating a synthetic infant video dataset, and fine-tuning video-based HMR methods. Requires Python and PyTorch skills.

Develop a general framework to characterize multi-view system observation capabilities. Tasks include implementing metrics for 3D/surface observation density and optimizing camera parameters using multi-view geometry and 3D vision. Requires strong Python/C++ skills.

This 12-month Energy Tech Accelerator Graduate Program offers two six-month missions to apply AI and emerging technologies. Candidates should have a Master's degree, exposure to AI, and be available to start in March 2027.

Internship to assess hybrid DRAM potential for energy-constrained systems. Design and evaluate critical circuit blocks like sense amplifiers using industry-standard tools. Requires Master's level background in electronics or microelectronics.

Internship focused on superconducting electronics for quantum computing. Tasks include technological review, functional analysis of QPU hardware, and circuit design/validation using JoSim. Requires an engineering or master's degree.

Develop methods to correlate experimental observations with RTL/netlist structures for pre-silicon security verification. Extend the $\mu$ArchiFI framework using experimentally grounded fault models based on laser injection campaigns.

Internship focused on preparing sub-30 nm FIB lamellae and utilizing limited-angle STEM tomography to reduce projection effects in nanoscale semiconductor devices. Requires Master 2 in materials science or physics with programming skills.

Internship focused on developing and applying Time-Resolved Cathodoluminescence (TRCL) for nanoscale characterization of III nitride microLED structures. Requires a solid background in solid state physics and English fluency.

Internship focusing on evaluating Graph Neural Networks coupled with radar sensor data. The role involves implementing and proposing innovative methods for processing radar data using AI algorithms.

Web Development Intern to build web solutions using Vue.js and Nuxt 3. Responsibilities include creating components, developing documentation, and enhancing web interfaces. Requires CS/Web Dev studies, HTML/CSS/JS knowledge, and Git familiarity.

Study state-of-the-art methods for modeling Voltage Controlled Filters (VCF). Test and compare non-linear solvers, from iterative to machine learning, for zero-delay feedback modeling. Requires strong DSP and electrical engineering knowledge.

Internship to explore LLM approaches modifying traditional static analysis for source code security evaluation. Define and experiment with new LLM-based methods, studying LLM complementarity with tools like Frama-C. Requires computer engineering background and knowledge of static analysis.