Postdoctoral Research Fellow in Advanced Artificial - San Francisco, United States - University of San Francisco

University of San Francisco
University of San Francisco
Verified Company
San Francisco, United States

3 weeks ago

Mark Lane

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Mark Lane

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Description

Postdoctoral Research Fellow in Advanced Artificial Intelligence for Equitable Safety of Vulnerable Road Users

University of San Francisco

R0009236

101 Howard

Job Title:

Postdoctoral Research Fellow in Advanced Artificial Intelligence for Equitable Safety of Vulnerable Road Users


Job Summary:


The University of San Francisco's Data Institute is seeking a Postdoctoral Research Fellow to contribute to the project titled "Advanced Artificial Intelligence Research for Equitable Safety of Vulnerable Road Users." This project aims to enhance traffic safety at intersections through real-time detection, classification, and tracking of vulnerable road users (VRUs) using advanced sensing technologies and artificial intelligence algorithms.

The appointment is tentatively expected to run for 1 calendar year, with the possibility of extension for an additional 6 months.


Full Job Description:


Responsibilities:

Research

  • Conduct research on advanced artificial intelligence techniques for the accurate detection, classification, and tracking of VRUs using video and point cloud data.
  • Develop innovative sensor calibration and fusion techniques to improve the precision and reliability of class perception mechanisms for VRUs.
  • Perform realtime motion analysis, conflict assessment, and risk assessment based on the status and predicted trajectory of VRUs.
  • Integrate Graph Neural Network (GNN) architectures for modeling relational dependencies among VRUs and improving trajectory prediction accuracy.
Development

  • Collaborate with interdisciplinary teams to design and implement algorithms and models for VRU detection and safety assessment.
  • Utilize deep learning methodologies for video and point cloud object recognition and detection.
  • Implement and optimize GNN architectures for dynamic graph modeling and trajectory prediction.

Qualifications:


  • Ph.
D. or equivalent experience in computer science, engineering, or related field.

  • Strong publication record demonstrating expertise in deep learning techniques related to video and point cloud object recognition/detection.
  • Familiarity with main deep learning packages such as TensorFlow, PyTorch, or Keras.
  • Proficiency in programming languages, particularly Python
  • Familiarity with Graph Neural Networks (GNNs).
  • Experience with sensor calibration, fusion techniques, and realtime motion analysis preferred.
  • Ability to work collaboratively in interdisciplinary teams.
  • Excellent analytical and problemsolving skills.

Submission Instructions:

Application Deadline
:
May 15, 2024

  • Research statement (not more than 3 pages)
  • Writing samples
  • CV or resume
  • Contact information for 3 potential recommenders
Applications will be accepted on a rolling basis, but early submission is encouraged.

The appointment is tentatively expected to run for 1 calendar year, with the possibility of extension for an additional 6 months.


Full-Time/Part-Time:

Full time


Pay Rate:

Salary


Salary Range:

$81,120 annualized

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