Computer Vision - Blacksburg, United States - Virginia Tech

Description

Job Description

Transportation is undergoing an incredible transformation that is inspiring researchers to approach problems using new technologies and non-traditional methods. At the Virginia Tech Transportation Institute (VTTI), a world-leading research enterprise, we are working with government agencies, transportation and safety organizations, vehicle manufacturers and their suppliers, and others to overcome these challenges and improve transportation safety and performance on the world's roads. ().

The Division of Data & Analytics (DDA) specializes in collaboration with industry, academic, and government partners to translate large-scale data collections into robust and timely guidance and decisions. The division focuses on challenging questions at the intersection of mechanical engineering, physics, computer science, statistics, behavior, performance, safety and policy. DDA projects leverage innovative data fusion approaches, algorithmic labeling processes, and interactive visualizations to translate disparate and highly dimensional data into visible progress and understandable results. The division's goals are to provide domain expertise and state-of-the-art data and analytic methods to enable our partners to answer their questions quickly, cost effectively, and with accessible output that is ready to address their most pressing needs.
VTTI seeks a qualified engineer/ scientist with a strong background and proven research record in big data analysis, computer vision, and machine learning with an interest in transportation research. The ideal candidate is an experienced researcher with a desire to eventually take a leadership role in a growing and diverse research portfolio. The candidate must be capable of working collaboratively in multidisciplinary teams to complete transportation research projects within constraints of time and budget. The research projects mostly include development of advanced computer vision and machine learning algorithm for perception, planning, and behavior modeling.

The candidate will join the team of "Computer Vision and Machine Learning" in DDA. The focus of the team is to process multimodal data including cameras and LiDAR to develop perception and safety model. Recently, the team is closely working with technologies related to automated driving systems and autonomous vehicles. Sensor fusion, scene perception, 3D scene understanding, pedestrian behavior tracking are some of the key problems that the team work closely with. The team also works with problems related to driver behavior analysis from camera data, and intersection safety analysis using multi-sensor system. The candidate will be closely working with other team members to develop and test novel algorithms in these areas.

The candidate must be motivated to work with problems related to transportation, safety, and operation using multi modal data. The candidate must also be proficient in algorithm development, software management, and deployment using image, videos, and time series data that are applicable to safety technology and automation. The candidate must also understand the working principles of advanced computer vision and machine learning methods like CNN, RNN, GAN, Autoencoders, Transformers, and reinforcement learning.

RESEARCH ASSOCIATE RANK – Research Associates will be expected to carry out the following duties and responsibilities:

• Literature reviews

• Multi-modal (Camera, LiDAR) data processing

• Big Data Processing

• Develop and manage software

• Interpret results and develop robust conclusions

• Write research reports

• Present research findings

• Assist in the development of proposals

• Assist with the conceptualization and creation of work plans, protocols, and procedures

• Coordinate and collaborate with subcontractors and interface with stakeholders and sponsors as required

•Must be able to work effectively independently with oversight as well as within multidisciplinary project teams as appropriate

All candidates must be able to work on-site/ in office at Blacksburg Campus for majority of the work hours (at least 4 days a week). Position may sporadically involve data collection including driving and operating test vehicles.

VTTI provides opportunities for professional advancement and a pay for performance compensation structure. The salary is commensurate with experience.

Required Qualifications

Research Associate rank will require an advanced degree in engineering, science, or related field. Demonstrated strong publication history in the field of computer vision, robotics are also encouraged to apply.

Technical:

• Extensive Working experience with computer vision (e.g. perception, tracking, semantic segmentation)

• Extensive knowledge and experience with training and testing of deep learning based models including CNN, RNN, AE, Transformers.

• Expertise in Python

• Expertise in big data analysis and high performance computing

• Extensive experience in software development and management using platforms like PyTorch, Tensorflow, OpenCV (4+).

• Peer reviewed publication in related field.
- Must have strong communication skills and organizational skills.
- Willingness to work in a fast paced, flexible research environment to solve complex problems and develop solutions that will improve safety on our nation's roadways.

Preferred Qualifications

• Previous experience in transportation research.

• Working experience with 3D point cloud and LiDAR data.

• Experience with Sensor fusion.

• Experience with 3D perception.

• Familiarity with standard dataset like WAD, Argoverse, KITTI, NuScenes.

• Experience with multiple programming languages (Matlab, Python, R etc.)

• Previous experience in mathematical modeling and simulation

• Knowledge of Docker (or similar) environment

• Knowledge of data visualization including web-based design

Appointment Type

Restricted

Salary Information

$70,000 - $85,000

Review Date

1/10/2024

Additional Information

The successful candidate will be required to have a criminal conviction check.

About Virginia Tech

Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight , a , a college, , and . The university has a significant presence across Virginia, including the in Northern Virginia; the Health Sciences and Technology Campus in Roanoke; sites in Newport News and Richmond; and numerous and . A leading global research institution, Virginia Tech conducts more than $500 million in research annually.

Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law.If you are an individual with a disability and desire an accommodation, please contact Erin Carter at during regular business hours at least 10 business days prior to the event. Advertised: November 27, 2023
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Position Location Closes Blacksburg, Virginia Are you excited to advance transportation through innovation and to work at the crossroads of computer vision, machine learning, and transportation research? You should join our team

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Position Location Closes Blacksburg, Virginia Are you excited to advance transportation through innovation and to work at the crossroads of computer vision, machine learning, and transportation research? You should join our team

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