Working Student in Data Science (m/f/d)

AP Sensing

Böblingen, GermanyHybridEnglish

You will be taken to the company's application page to apply directly.

Required skills

Artificial IntelligenceSelf-supervised LearningPyTorchMachine LearningHyperparameter TuningDataset CurationDeep LearningFoundation ModelsModel Architecture DesignPython

Job description

Our Company

WE ARE AGAIN AWARDED AS TOP ARBEITGEBER 2025!

At AP Sensing, we’re building on a success story that began in the garage of Bill Hewlett and Dave Packard. As an independent HP spin-off, we combine over 40 years of technological expertise with a passion for innovation.

Together we’ll continue to enhance infrastructure, protect people, and safeguard our planet. With a commitment to fiber optic sensing, we value every voice and empower growth. Join us in thinking ahead to create a smarter, safer world.

YOUR NEW CAREER

Your topic: Foundation Models for Distributed Acoustic Sensing (DAS)

DAS enables long-range and high-resolution measurements along optical fibers by converting them into arrays of thousands of virtual microphones. This technology is used in a wide range of applications, including critical infrastructure monitoring and environmental monitoring. However, DAS systems generate huge amounts of high-resolution data, creating significant challenges for real-time processing and analysis.

Your Responsibilities

Define the model architecture and self-supervised learning methods for foundation models.
Build and curate a diverse and multi-domain DAS dataset.
Run an iterative train-evaluate-optimize loop to experiment with self-supervised algorithms, hyperparameters, and model sizes.
Evaluated the benefits of self-supervised foundation models against existing models using the benchmark DAS dataset.

Your Profile

You are a master's student (m/f/d) looking for a working student position and are capable of independent work without extensive hand holding.
You are interested in the topic of Foundation models for Distributed Acoustic Sensing.
Academic background in computer science, mathematics, physics, or electrical engineering.
Very good knowledge of machine learning, artificial intelligence, and, optimally, the concepts of foundation models.
Strong programming skills in Python and hands-on experience with machine learning frameworks such as PyTorch.
Experience with deep learning and practical project experience is required.
Interest in applied research and real-world data challenges.
Duration: 6 months / 20 hours per week.

Our Offer