Currently, HRI-US (Silicon Valley) is offering research internships to highly motivated Ph.D. (and qualified M.S.) students.  Interns will work closely with HRI researchers, and publishing results in academic forums is highly encouraged.  We are looking for candidates with good publication track records and excellent programming skills to join our team!

Highly Sensitive and Selective Gas Sensors (Job Number: P18INT-20 )
San Jose, CA

This position offers the opportunity to develop highly sensitive and selective gas sensors based on nanoparticles and carbon nanotubes. The candidate must possess excellent interpersonal and communication skills, eagerness to learn and grow, and have a flexible approach to solving problems.  


​Job Responsibilities:

  • ​Participate in developing the next generation of gas sensors targeting challenging applications such as environmental gas sensing
  • Establish automated gas sensor measurement and gas flow control through a computer
  • Carry out senor performance studies upon exposure to various gas species
  • Work with supervisor in resolving sensor signal detection and characteristic process problems
  • Present the results through presentations and scientific publications

Qualifications:

  • ​Ph.D. candidate in Electrical Engineering, Materials Science, Physics or related field
  • Hands-on experience in electrical measurements and familiarity to lab tools such as oscilloscope and data logger
  • Experience with analog circuit design, firmware programming, and circuit board design
  • Excellent ability to identify and solve problems
  • Strong written and oral communication skills, good team worker

Duration:

  • 4 – 6 months
Perception for Manipulation (Job Number: P19INT-01 )
San Jose, CA

This title includes multiple positions which focus on formulating and developing algorithms, and running experiments to advance research in human-centered robotic manipulation in the context of home robotics. The candidate is expected to work on one of the following topics:

  • Robotic manipulation using vision and tactile sensors in unstructured environments
  • Developing algorithms that allow robots to manipulate deformable objects cooperatively with humans using learning from demonstration and reinforcement learning techniques

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, or related field
  • Experience in deep learning and other machine learning methods
  • Experience in the following is preferred for some topics: tactile sensing, manipulation, grasping, motion planning, real robots, or physics engines such as MuJoCo and Bullet
  • Good programming skills in either C++ or Python
  • Experience in Robot Operating System (ROS)
Symbolic Planning from Vision (Job Number: P19INT-02 )
San Jose, CA

This position focuses on developing algorithms for symbolic planning from vision. Performing real-life tasks in everyday settings requires an agent to plan goal-conditioned decisions sequentially based on its perception of the environment. Existing works typically solve this problem by decoupling perception and
planning. While recent works have shown promise in coupling the two tasks, simple tasks like push or stack objects are studied. In this work, we aim to go one step ahead to tackle complex tasks requiring sequential decision processes from vision. The candidate will work on the development of algorithms,
establishment of benchmark environments, evaluation of the proposed algorithms, and ultimately deployment of the proposed algorithm to real robot systems.

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, mechanical engineering, or related field
  • Strong familiarity and research experience in some or all of the following fields: structured representation learning from videos, and task and motion planning.
  • Excellent programming skills in C++ or Python
  • Hands-on experience on deploying algorithms to real robot systems preferred
Physical Human-Robot Interaction (Job Number: P19INT-03 )
San Jose, CA

This title includes multiple positions which focus on formulating and developing algorithms as well as running user studies of teleoperated / autonomous physical human-robot interaction (pHRI). The candidate is expected to work on one or more of the following topics:

  • Hardware and software integration
  • Interaction design and modeling of pHRI
  • Human intention and emotion recognition for pHRI
  • Planning, preparation and execution of user studies

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, mechanical engineering, or related field
  • Experience in robot kinematics/dynamics, force control, or human motion analysis
  • Experience in setting up and executing real robot experiments using ROS
  • Experience in setting up and executing user studies involving robot hardware
  • Excellent programming skills in C++ or Python
Social Navigation (Job Number: P19INT-04 )
San Jose, CA

This internship position focuses on constructing movement algorithms for robots in human populated environments that considers both classical metrics (e.g., safety and efficiency) and social robotics metrics (e.g., human proximity preference, multifaceted human intent, and human flexibility).

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, or related field
  • Experience in machine learning (adversarial reinforcement learning/Bayesian neural networks, and stochastic processes such as Dirichlet and Gaussian processes), control theory, and optimization
  • Experience training and testing on existing public datasets (ETH, UCY)
  • Ability to assist in the collection of a new benchmark dataset for social navigation (to be used for validation, and, potentially, training of new algorithms)
Exploration of Spatial Representations in the Scene (Job Number: P19INT-05 )
San Jose, CA

This title includes multiple positions which focus on exploring object-level state representations to describe the scene. The candidate is expected to work on one or more of the following topics:

  • Exploring learnable modes (walking, skipping, or hopping) for agents
  • Discovering object-level representations of the scene to formulate priors for predictions
  • Designing adaptive spatial representations that better focus explorations in higher dimensions

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, mechanical engineering, or related field
  • Strong familiarity and research experience in some or all of the following fields: Machine Learning, Reinforcement Learning, knowledge representations, and video prediction
  • Excellent programming skills in C++ or Python
Interactive Decision Making (Job Number: P19INT-06 )
San Jose, CA

This project focuses on the development of planning and control algorithms for interactive decision making. We will be expanding on our work on interactive decision making for merging and extending it to deadlock scenarios. We would like to continue this research to increase the robustness under different
operating conditions. This is an opportunity for applied work in MPC, game theory, and safe RL.

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, or related field
  • Strong familiarity and research experience in path planning, and control of robotic systems
  • Highly proficient in software engineering using C++ and/or Python
Signal Processing Brain Computer Interface Sensor Research (Job Number: P19INT-07 )
San Jose, CA

This project focuses on the processing and training models using noisy sensors. The researcher will investigate detecting drowsiness, arousal, and other attributes from Brain-Computer Interfaces (BCI).  Experience with deep learning, sensor hardware, and physical sensor processing are preferred.

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, or related STEM field
  • Strong familiarity and research experience in signal processing, and filtering raw sensor signals
  • Experience with machine learning for signal processing for EEG, bio signals and related sensor data is strongly preferred
  • Hands-on design and system implementation skills, including real-time data processing
  • Highly proficient in software engineering using C++ and/or Python
Human Machine Interface (HMI) and Behavioral Research (Job Number: P19INT-08 )
San Jose, CA

This project focuses on design, implement and evaluate HMIs for next generation of mobility. The researcher will investigate detecting drivers’ mental cognitive workload (distraction), drowsiness, emotion, and other states and correct their driving impairment. Experience with simulation environments, experimental design, behavioral research and user studies is preferable.

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in human computer interaction, human factor engineering, psychology, statistics/biostatistics or related STEM field
  • Familiarity and research experience in experimental and survey design 
  • Knowledge of statistics
  • Hands-on experience in designing, implementing and evaluating Human Machine Interface (HMI) or Human Computer Interface (HCI)
  • Proficient in statistical computer languages (R, Python, SQL, etc.)
Multi-modal Sensor Fusion using Deep Neural Networks (Job Number: P19INT-09 )
San Jose, CA

We perceive the environment through various sensors, such as cameras, lidars and radars. Since these sensors have complementary properties, the best results can be achieved by fusing multiple types of sensors. In this project, we attempt to first understand the approach taken by state-of-the-art deep fusion networks, and further improve its performance in challenging scenarios. In addition, we want to output additional information such as main contributor to each detection, uncertainty, etc.

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, or related field
  • Strong familiarity with computer vision and machine learning techniques pertaining to feature extraction, 2D/3D object detection, and region proposal
  • Hands-on experience in one or more of the following: LiDAR/radar processing and sensor fusion
  • Experience in open-source deep learning frameworks such as TensorFlow or PyTorch preferred
  • Excellent programming skills in Python or C++
Human Behavior Understanding and Prediction (Job Number: P19INT-10 )
San Jose, CA

The title includes multiple positions which focus on developing computer vision and machine learning algorithms for analysis, prediction, and understanding of human behavior in various domains to support on-going research on next generation intelligent mobility systems. The candidate is expected to work on one of the following topics:

  • Human behavior understanding in a public environment
  • Action recognition and prediction using single- or multi-modal data
  • Human motion (trajectory and pose) and intention prediction in indoor and outdoor environments
  • Future trajectory forecast from a top-down and/or perspective view
  • Detecting and recognizing human-object interaction
  • Driver state / activity understanding for in-cabin monitoring
  • Creation of a benchmark human behavior dataset for various applications

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, or related field
  • Strong research experience in computer vision and machine learning
  • Hands-on experience in one or more of the following: trajectory forecast, motion prediction, behavior understanding, activity recognition, pose estimation/tracking, intention prediction
  • Experience in open-source deep learning frameworks such as TensorFlow or PyTorch preferred
  • Excellent programming skills in Python / C++ / Matlab
Computer Vision and Machine Learning in Traffic Scenes (Job Number: P19INT-11 )
San Jose, CA

The title includes multiple positions, which focus on developing computer vision and machine learning algorithms to capture the detailed semantics of 2D and/or 3D traffic scenes. The candidate is expected to work on one of the following topics:

  • Capturing the semantics of visual scenes by explicit modeling of objects, their attributes, and relationships to other objects and the environment
  • Segmentation, recognition, registration, and tracking through fusion of 2D video and 3D LiDAR point cloud data
  • Analysis, reconstruction, and interpretation of 3D dynamic scenes
  • Higher level classification/recognition of dynamic traffic scenes including place, conditions, and spatial relationships using temporal event detection, action recognition, and localization
  • Multi-modal sensor fusion (cameras, LiDARs, and radars) for detection / classification with uncertainty
  • Driver attention and risk perception modeling from video in urban traffic scenes

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, or related field
  • Strong familiarity with computer vision and machine learning techniques pertaining to scene understanding, image classification, and object detection
  • Hands-on experience in one or more of the following: probabilistic graphical models, scene graphs, spatio-temporal graphs, SLAM, sensor fusion, visual recognition, video classification, LiDAR processing
  • Experience in open-source deep learning frameworks such as TensorFlow or PyTorch preferred
  • Excellent programming skills in Python or C++
  • Experience with Point Cloud Library (PCL), Robot Operating System (ROS), and GPU programming is preferred for some topics
Video-based Captioning and Retrieval in Traffic Scenes (Job Number: P19INT-12 )
San Jose, CA

This title includes multiple positions, which focus on developing computer vision and machine learning algorithms to describe (or caption) complex or rare events that are important in traffic scene understanding and enable retrieval of these events based on key-words, linguistic description, graphical representation, or video similarity based search methods. The candidate is expected to work on one of the following topics:

  • Generating natural language description of traffic scene events that impact navigation (e.g. accidents, construction, road blockage) from video inputs
  • Constructing a dataset (synthetic and/or real) to support activities in video-based captioning of traffic scene

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, or related field
  • Strong familiarity with computer vision and machine learning techniques pertaining to video captioning and retrieval
  • Familiarity with creating datasets for video captioning, including visual question and answering methods is preferred for one position
  • Experience in open-source deep learning frameworks such as TensorFlow or PyTorch preferred
  • Excellent programming skills in Python or C++
Multimodal Signal Processing for Driver Behavior Analytics (Job Number: P19INT-13 )
San Jose, CA

This project focuses on development of multi-modal machine learning algorithms for characterization of driving styles or behaviors unique to individuals. The project involves recognition of driver errors, states and characteristics via analyzing data from car sensory, driver physiology, and driver behavior data.

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, or related field
  • Strong familiarity and experience in machine learning, data analytics and signal processing
  • Experience in open-source deep learning frameworks such as TensorFlow or PyTorch preferred
  • Highly proficient in C++ and Python
  • Experience / interest in on-the-road social psychology preferred
Explainable AI (Job Number: P19INT-14 )
San Jose, CA

This title includes multiple positions which focus on behavior cloning models using computer vision and machine learning algorithms with emphasis on human-interpretable network. The candidate is expected to work on one of the following topics:

  • Utilizing semantic modeling, reasoning, and inference paradigm to explain model output
  • Exploiting driving domain knowledge based constrains to ensure safety and rule following

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, or related field
  • Strong familiarity with computer vision and machine learning techniques pertaining to XAI
  • Experience in one or more of the following topics: Network attention, graph neural network, knowledge/scene graph preferred
  • Experience in open-source deep learning frameworks such as TensorFlow or PyTorch preferred
  • Excellent programming skills in Python
Vision and Language (Job Number: P19INT-15 )
San Jose, CA

This project focuses on developing vision and language algorithms to advance research in vision-language navigation. The project involves developing algorithms that interpret visually-grounded natural language instructions and conduct driving/in-house navigation tasks based on the input text.

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, or related field
  • Strong familiarity with computer vision, natural language processing and machine learning techniques pertaining to vision and language navigation
  • Experience in open-source deep learning frameworks such as TensorFlow or PyTorch
  • Excellent programming skills in Python
Computational Model for Social Human-Machine Interaction (Job Number: P19INT-16 )
San Jose, CA

This project focuses on computational model for social human machine interaction in human supervisory control scenarios. The project involves design and development of interaction model using probabilistic models and optimization of the agent behavior using supervised and reinforcement learning algorithms.

Responsibilities:

  • Modeling Behavior Dynamics for social human machine interaction
  • Build behavior modeling framework to understand and predict interactions between human and machine.
  • Develop more usable machine/deep learning tools for improve system performance and mobility safety

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in computer science, electrical engineering, mathematics, statistics, psychology, cognitive science or human behavior related field
  • Research experience in machine learning, behavioral research and human-computer interaction
  • Excellent programming skills in either C++ or Python
  • Experience in applied statistics i.e. probabilistic models and Bayesian models, machine/deep learning, reinforcement learning and human-in-the-loop online learning
  • Experience in open-source deep learning frameworks such as TensorFlow or PyTorch preferred
Human Factors for Next Generation Mobility Interfaces (Job Number: P19INT-17 )
San Jose, CA

This position offers the opportunity to design and conduct human-factors study for next generation mobility HMIs on our experimental simulator setups.

Responsibilities: 

  • Design and conduct the human-factors studies to evaluate our prototype Human Machine Interactions (HMIs)
  • Generate insights that both fuel ideation and evaluate designs
  • Data analysis to compare subjects’ driving behavior and perception of systems.
  • Conduct research using a wide variety of quantitative methods, and interpret analysis through the lens of HMI, and social science

Qualifications:

  • Ph.D. or highly qualified M.S. candidate in human behavior related field (human computer interaction, human factor engineering, cognitive science), psychology, or social science
  • Familiarity with human-machine interface, interaction/behavioral modeling for automobiles
  • Experience in 3D graphics engines such as Unreal Engine 4 or Unity preferred
  • Experience in experimental design, human behavioral signals from human monitoring sensors such as head-pose detection, eye tracker and physiology measurement sensors
  • Experience in hypothesis test, probabilistic models and survey data analyses
  • Research experience in automotive HMIs
Nanoparticle Synthesis (Job Number: P19INT-18 )
San Jose, CA

This position offers the opportunity to work on nanoparticle solution synthesis, characterization, and surface modification. The candidate must possess excellent interpersonal and communication skills, eagerness to learn and grow, and have a flexible approach to solving problems.

Responsibilities:
Participate in exploring and developing efficient methods for the solution synthesis and surface modification of inorganic nanomaterials such as metal nanoparticles, semiconductor nanoparticles, and their alloys

  • Develop simple methods to improve the dispersion of inorganic nanomaterials
  • Design organic grafting reaction on nanoparticle surface
  • Characterize and analyze the nanomaterials through SEM, XRD, Raman, UV-Vis, FT-IR, TGA, and so on
  • Work with supervisor, as needed, in resolving synthetic and surface modification process problems
  • Demonstrate efficient and safe work practices with a high level of diligence
  • Present the results through presentations and scientific publications

Qualifications:

  • Ph.D. students in chemistry, materials science or related field, with rich experiences in solution synthesis, separation of nanomaterials and characterization
  • Experience with surface science and functionalization, surface chemistry is a plus
  • Strong written and oral communication skills, good team worker

Duration:
4-6 months

Fabrication of Battery Electrodes (Job Number: P19INT-19 )
San Jose, CA

This position seeks a qualified candidate for an Intern position to begin from February 2020. The candidate
must possess excellent interpersonal and communication skills, eagerness to learn and grow, and have a
flexible approach to solving problems.

Responsibilities:

  • Assisting in fabrication of self-standing electrodes based on composite sheets
  • Characterize and analyze the materials through DSC/TGA, BET, Raman, SEM, etc.
  • Manage experimental data and present the results through reports, presentations and scientific publications

Qualifications:

  • M.S. or Ph.D. in materials science, physics, chemistry, or a related field
  • Hands-on experience in carbon nanotube synthesis and related characterization techniques
  • Excellent general lab skills and safety practices
  • Data management

Duration:
6 month

How to apply

​Please send an email to careers@honda-ri.com with the following:

  • ​​Subject line including the ​job number you are applying for
  • Recent CV
  • A cover letter explaining how your background matches the qualifications
Candidates must have the legal right to work in the U.S.A.​