maulik jagtap

Fähigkeiten und Kenntnisse

CNN + Weight
Machine learning
camera calibration
computer vison
Maschinelles Lernen
RGB Image
CNN + Physical Properties
Computer-Vision
Multimodel classifcation
TensorFlow
MVCNN
Data Augmentation
Tkinter
object detection
Depth Image
PosEstimation
Transfer learning
digitilization
Positional Encoding
drone cameras
Python
Fusion
PyTorch
image classification
Realsense Cameras
Keras

Werdegang

Berufserfahrung von maulik jagtap

  • Bis heute 2 Jahre und 7 Monate, seit Jan. 2024

    Software Engineer & Technology Consultant

    PROSTEP AG

    I work at the intersection of enterprise software engineering and technical consulting, helping global clients adopt secure, scalable digital collaboration solutions. Key Contributions: • Design and implement backend software solutions aligned with complex enterprise and regulatory requirements • Act as technical consultant for GlobalX, a secure platform for cross-company communication and data exchange • Translate client business needs into robust system architectures and production-ready implementat

  • 10 Monate, März 2023 - Dez. 2023

    Software Engineer – Machine Learning & Digital Twin Systems

    PROSTEP AG

    Developed and deployed machine learning solutions for 3Digital Twin, an enterprise platform aimed at automating and optimizing industrial engineering processes. Key Contributions: • Designed and implemented CNN-based machine learning models to transform 3D point cloud data into precise CAD models • Built scalable data preprocessing and feature engineering pipelines to improve model accuracy and robustness • Reduced manual CAD modeling effort by enabling automated geometric feature extraction • Analyzed lar

  • 10 Monate, März 2022 - Dez. 2022

    Master’s Thesis – Industrial Object Recognition

    Fraunhofer IPK

    Master’s thesis focused on improving industrial object recognition by fusing visual data with physical object properties. • Developed CNN-based classification models using RGB and depth images • Implemented multi-view CNN (MVCNN) architectures to improve robustness across object orientations • Fused physical properties (height, width, depth, weight) with visual features to improve classification accuracy • Worked with Intel RealSense RGB-D cameras for data acquisition • Implemented camera calibration and p

  • 7 Monate, Sep. 2021 - März 2022

    Working Student – Computer Vision

    Fraunhofer Institute for Production Systems and Design Technology IPK

    Worked as a student researcher supporting AI-based industrial object recognition projects. • Generated synthetic image datasets to improve model generalization • Developed a Python-based GUI (Tkinter) for automated image acquisition • Integrated Intel RealSense cameras for synchronized RGB-D data capture • Performed camera calibration and preprocessing for computer vision pipelines • Supported data preparation for CNN training workflows

  • 6 Monate, März 2021 - Aug. 2021

    Student Internship

    Chemnitz University of Technology

    Automated Forest Inspection of dead and dying trees affected by bark beetles.

Ausbildung von maulik jagtap

  • 2019 - 2022

    Master's degree

    Chemnitz University of Technology

  • 2016 - 2019

    Bachelor's degree

    Government Engineering College, Modasa

  • 3 Jahre und 2 Monate, Aug. 2013 - Sep. 2016

    Diploma of Education

    Government Polytechnic Ahmedabad

Sprachen

  • Englisch

    C2 (Verhandlungssicher / Muttersprachlich)

  • Deutsch

    B1-B2 (Gute Kenntnisse)

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