Sensor Researcher / Data Analysist (m/v/d)

Mine Kafon Lab
Maastricht, Netherlands
8 days ago

Role details

Contract type
Permanent contract
Employment type
Part-time (≤ 32 hours)
Working hours
Shift work
Languages
English
Experience level
Intermediate

Job location

Maastricht, Netherlands

Tech stack

Artificial Intelligence
C Sharp (Programming Language)
Computer Programming
General Packet Radio Service
Global Positioning Systems (GPS)
Python
Matlab
Machine Learning
OpenGL
TensorFlow
System Software
Digital Twin
PyTorch
Scikit Learn
Information Technology
Software Library

Job description

Our sensor-research team is responsible for advancing Mine Kafon's end-to-end UXO-detection pipeline. Using metal detectors, magnetometers and ground-penetrating radar (GPR), we design and run field trials, develop data-cleaning procedures, and design algorithms that automatically locate anti-personnel and anti-tank landmines.

Processed results feed directly into our Mine Kafon Ground-Control Software (MKGCS), giving even non-expert operators the opportunity of reliably determinig the positions of landmines.

Your goal will be to help us build a more robust, accurate, and eventually fully classifying, detection workflow, in which you will have complete freedom, you decide your research focus, as long as it benefits our capabilities. Possible avenues include: attenuating noise from sensitive sensors, estimating burial depth from multi-probe magnetometer data, rendering 3D GPR volumes, or any other experiment that sharpens sensor performance, improves automatic detection or improves non-expert usability of the software.

Requirements

  • Bachelor's, master's, or PhD in Data Science, Physics, Geophysics, Computer Science, Artificial Intelligence, or another closely related study.
  • Familiarity with programming (C#/Python/MATLAB/etc).
  • Working knowledge of digital signal-processing concepts (filter design, FFT, time-frequency analysis).
  • Ability to perform careful field measurements with scientific rigour (survey design, metadata capture, basic statistics).
  • Clear, well-structured communication and documentation habits; comfortable collaborating in a multidisciplinary team.
  • Solid high-level math background.

Nice-to-have skills (optional, though beneficial):

  • Practical experience with metal detectors, magnetometers, GPRs or similar hardware.
  • Familiarity with machine-learning or statistical-learning libraries (scikit-learn, PyTorch, TensorFlow).
  • 3-D data-visualisation experience (e.g., Unity, PyVista, OpenGL, VTK).
  • Knowledge of geospatial tools (GIS, coordinate transforms, GPS post-processing).
  • Understanding of electromagnetic theory relevant to UXO detection (dipole models, skin depth, magnetic gradients) for possible development of a digital twin.

Benefits & conditions

Company pension

  • Flexible work hours
  • Company laptop, What will you get:
  • Opportunity to work on a social humanitarian award winning project.
  • Market-competitive salary
  • Working with a dynamic, international team in a growing company.
  • You will be directly involved in development of the product and take responsibility for your tasks.
  • You will have the opportunity to learn how to manage complex projects with a great team.
  • You will be working closely with the founders of the company.

About the company

Mine Kafon is an R&D Lab where innovation takes place. With a multidisciplinary team, sustainable solutions for environmental and social problems are developed. You will be mainly involved in the Mine Kafon Drone project (MKD). The MKD is developed to substitute current dangerous demining techniques and reach the goal of a landmine free world in 10 years. The MKD flies over dangerous areas; maps it, detects and detonates landmines from a safe distance. The drone works autonomously, equipped with a multitude of interchangeable robotic extensions and sensors. These new methodologies combined make the MKD up to 20 times faster than traditional demining technologies. Apart from being a safer solution, it is also up to 200 times cheaper than existing demining techniques.

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