Qualifications:

　　An MS/PhD in Computer Science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline. PhD strongly preferred.

　　Should have experience in feature engineering, hyper parameter tuning, model evaluation etc.

　　Good exposure to machine learning/text mining tools and techniques such as Clustering/classification like SVM, Deep Learning networks like FRCNN, MRCNN, ResNet, FVRCNN, SalsaNext, NASnet, LSTM Reinforcement learning, and other numerical algorithms

　　Should have experience in using Pandas/Numpy/ScikitLearn, Pytorch, Tensorflow, Keras, ROS, Gazebo, OpenJAUS

　　Practical knowledge of automotive sensors like Camera, RADAR etc.

　　Sound knowledge on the Driver assistance systems (feature functions like lane departure prevention, collision avoidance etc.)

　　Strong theoretical knowledge of detection, segmentation, obstacle detection, graphical methods, probabilistic algorithms or optimization

　　Hands on with visualization tools for sensors

　　Knowledge in developing, calibrating and testing multi-sensor systems

　　Applied knowledge of point cloud or Lidar based algorithms such as segmentation, localization, filtering

　　Strong background and understanding of mathematical concepts relating to probabilistic models, conditional probability, numerical methods, linear algebra, neural network under the hood details

　　Familiarity with data science toolkit such as jupyter lab/notebooks, pandas, bash scripting, Linux environment

　　Publications and presentations in recognized (CVPR, NIPS, ECCV, ICML) Machine Learning journals/conferences is a Big plus

　　Familiarity with any one programming (e.g., C/C++/Java) or scripting (R/Python) languages

　　Excellent problem solving, communication, and data presentation skills

　　Key Roles and Responsibilities of Position:

　　Applying various Deep learning networks, statistical techniques, explore and experiment on new models through research papers or via various frameworks

　　Understanding, transforming large scale data to usable form for modelling, filtering data with generalization for later use, Cross-validating models for the requirements.

　　Recommend and justify the algorithms to implement for the problems at-hand

　　Implement libraries, algorithms, and tools for processing Lidar data to push the state-of-the-art in obstacle detection, object tracking, and related perception challenges

　　Developing solutions for 3Cs Competitive, Cooperative and Complementing sensor framework projects

　　Build perception pipeline fusing Camera, LIDAR, RADAR data for 2D and 3D object detection, scene segmentation, classification, tracking, event classification and motion predictions

　　Research and develop algorithms for sensor fusion and object association across multi-sensor modalities such as one or more cameras, radars, and Lidar sensors.

　　Perform multi-target tracking through the lifecycle of tracked objects including creation, splitting and merging, and termination of tracked objects.

　　Enhance deep learning networks with multi-GPU and multi-node capabilities

　　Interact with internal stakeholders to understand the business problems

　　Applying calculus, algebra and other math to build reliable, scalable model.

　　Automate algorithms in production through standardization of process and authoring best practices

　　Producing and disseminating technical and non-technical reports that detail the successes and limitations of each project.

　　Requisition ID : 23406