AI tech internship:
Neural Network Optimization for Indoor Localisation

Robust deep learning for RF-based positioning

Are you an ambitious student fascinated by artificial intelligence (AI) and excited to solve real-world localisation challenges?

This internship is perfect for students eager to work hands-on with machine learning and RF-based indoor positioning, improving the accuracy and robustness of an operational AI system.

Apply now for this internship!

About this internship

Crownstone’s indoor localization engine currently leverages multiple classifiers to determine user position based on wireless signal data. Although the system is already deployed in various environments, performance can still be improved.

Your internship will focus on optimizing the current NN model and enhancing the processing pipeline, addressing practical issues such as noise, latency, confidence scoring, and prediction drift.

Your work will directly contribute to the next generation of Crownstone localisation engine running on edge devices like the Raspberry Pi.

What will you do and learn?

This internship uniquely combines applied research with direct improvements to a production-ready system.

You will:

Investigate and implement advanced preprocessing techniques for RF signal features.
Upgrade the current NN to a more robust, noise-resilient architecture.
Benchmark and compare performance with the existing classifiers.
Improve postprocessing strategies (e.g., smoothing, filtering, confidence measures).
Work closely with experienced engineers applying AI techniques to real-world IoT systems.
Gain insight into scalable ML workflows beyond pure experimentation.

About you

You do not need to be an expert yet.

Intellectual curiosity and a proactive mindset are key.

You are likely a great fit if you:

Are currently studying Computer Science, Artificial Intelligence, Electrical Engineering, Applied Mathematics, or a related field.
Are in the final phase of your bachelor’s or pursuing a master’s degree.
Enjoy developing and optimizing machine learning models.
Are proficient in Python and familiar with deep learning frameworks (PyTorch, TensorFlow).

It’s a bonus if you:

Have familiarity with signal processing or wireless communication data
Have experience with real-world datasets and model validation.
Have participated in research projects or hackathon

If you want to help improve indoor positioning technology used in everyday smart spaces, this internship gives you a genuine opportunity to have impact.

What can you expect from us

About Almende and Crowntone

This internship is at one of our daughter companies, namely Crownstone.

Crownstone is revolutionizing indoor location tracking, transforming how businesses and technologies interact with physical spaces.

Our technology is used for indoor localization, enabling the tracking of various devices like tags, smartphones, and other BLE devices.

Our culture

We are a small-scale and flat organization. The culture is informal, and communication lines are short. We don’t like office politics.

At Almende and Crownstone, employees are given the space to work independently. We empower our employees to voice their ideas. If you enjoy taking initiative, have many ideas, and wish to execute them, you’ll feel right at home.

What we offer

Real hands-on experience: Apply what you’ve learned in your studies to real-life situations
A peek into the international tech scene
Build a network with industry pros
Guidance and mentorship from people who’ve been in your shoes

The application process

Apply

Send us your resume at teresa@crownstone.rocks

First meeting

We’ll first check if your profile fits. If yes, you’ll meet with the Head of the department.

Second meeting

Meet one of your future colleagues for an in-depth talk about the subject matter you applied for.

Offer

If there’s a mutual connection, we’ll make you a great offer. Hopefully, we can welcome you to our team soon!

AI tech internship: Neural Network Optimization for Indoor Localisation