AI Scientist

Posted by Human Resources
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Bengaluru (Bangalore)
3 - 7 yrs
₹7L - ₹20L / yr (ESOP available)
Skills
Artificial Intelligence (AI)
Deep Learning
Artificial Neural Network (ANN)
Python
Machine Learning (ML)
PyTorch
TensorFlow
CUDA
Keras
computer vision
CNN
Transfer Learning
Object Detection
Job description

The role involves computer vision tasks including development, customization and training of Convolutional Neural Networks (CNNs); application of ML techniques (SVM, regression, clustering etc. ) and traditional Image Processing (OpenCV etc. ). The role is research focused and would involve going through and implementing existing research papers, deep dive of problem analysis, generating new ideas, automating and optimizing key processes.

 

Requirements:

  • 2 - 4 years of relevant experience in solving complex real-world problems at scale via deep learning, computer vision or AI
  • Python, cuDNN, Tensorflow/PyTorch/Keras (or similar Deep Learning frameworks).
  • CNNs, RNNs, Transfer learning (for image classification, segmentation, object detection etc).
  • Image Processing techniques using OpenCV or other white-box image feature extraction algorithms.
  • End to end deployment of deep learning models.
About Synapsica Healthcare

At Synapsica, we are creating AI-first PACS and radiology workflow solution that is fast, secure and automates reporting tasks, helping radiologists create high quality reports quicker.

Our goal is to enable radiologists with fast, easy to use AI technology that helps generate high quality, evidence-based reports to significantly improve patient care.

Synapsica is a growth stage HealthTech startup founded by alumni from AIIMS, IIT-KGP & IIM-A with a vision to increase the accessibility of diagnostic services globally. We are solving the problem of shortage of radiologists by bringing in automation at various stages of the radiology reporting process to reduce reporting time, costs & error rates & deliver efficiencies for better patient care.

We are deploying Computer Vision based Neural Networks models to identify biomarkers of pathologies in Radiology scans. These models not just identify pathologies, but provide detailed characterization of what exactly constitutes that pathology.


RADIOLens
is an intuitive, AI enabled, cloud based RIS/PACS solution that makes diagnostic radiology workflows smoother. It provides Faster image uploading with Zero loss in image quality. RADIOLens helps automate mundane reporting tasks so radiologists can focus on clinical correlations for their patients.

Spindle for MRI Spine helps with reporting of age related degeneration of spine. It automatically identifies key spinal mensurations and provides a detailed, standardized report with annotated images of abnormal various spinal elements. It quickly identifies variations and pathologies, such as spinal deformations, degeneration, identification of listhesis, etc.

SpindleX
for stress X-Rays of spine takes automation a step further by generating a one click automated reporting for all XR cases. These reports quantify all abnormalities and features of injury or early degeneration such as spinal instability, abnormal intersegmental motion etc. Illustrative graphs and tables comparing extent of injury against standards are automatically included in the report making it easier to generate qualitative reports with visual evidence.

Crescent
segregates normal, abnormal and bad quality captures in chest X-rays. It identifies bad quality scans that need re-capture and for good scans it localizes and characterizes common lesions & abnormalities. With Crescent, standard, pre-filled reports are generated for normal radiographs and critical studies are prioritized in the worklist.

We are backed by Y Combinator and other investors from India, US and Japan. We are proud to have GE, AIIMS, the Spinal Kinetics as our partners.

Here’s a small sample of what we’re building: https://youtu.be/MtWSF-x2sxY


Join us, if you find this as exciting as we do!

 
 
 
 
Founded
2019
Type
Product
Size
20-100 employees
Stage
Raised funding
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