Opportunities for students

Your potential. Our future.

Big part of our success is owed to developing young talents and supporting your growth with our know-how. And it's not a one-way street. We learn a lot from you and your ability to see things from a different angle. Together we are better!

 

Internships

Please find our open internships on our job openings page here.

 

Upcoming job fairs or events

Please look up if we soon have a job fair or an event (face to face or virtual). Come and exchange with us to learn more about u-blox and its opportunities: upcoming events.

 

Student thesis

About u-blox

u-blox (SIX:UBXN) is a global provider of leading positioning and wireless communication technologies for the automotive, industrial, and consumer markets. Their solutions let people, vehicles, and machines determine their precise position and communicate wirelessly over cellular and short-range radio networks.

With a broad portfolio of chips, modules, and a growing ecosystem of products supporting data services, u-blox is uniquely positioned to empower its customers to develop innovative solutions for the Internet of Things, quickly and cost effectively. With headquarters in Thalwil, Switzerland, the company is globally present with offices in Europe, Asia, and the USA.

 

Man with glasses and woman looking at a module in front of a monitor

Project 1: Joint Bluetooth direction finding and distance estimation

Bluetooth is an efficient and low power technology for connectivity and indoor positioning. There is recent progress in the Bluetooth standardization for direction finding and channel sounding estimation technologies. The direction finding enables angle of arrival (AoA) estimations and the channels sounding enables distance estimations.

The topic of this thesis is to evaluate combinations of Bluetooth AoA and distance estimation techniques for enhanced positioning accuracy and new use cases. The joint positioning algorithm will be based on simultaneous or sequential inputs of AoA and distance estimations. The algorithm will be evaluated in Matlab with the target to be embedded in wireless MCU modules. The thesis work will focus on how accuracy can be enhanced with sophisticated joint algorithms and localization techniques.

Candidate profile:

  • Background in digital communications and wireless short range radio technologies
  • Familiar with digital signal processing techniques
  • Knowledge of wireless positioning methods (angle-of-arrival (AoA), round-trip time (RTT))
  • Good knowledge of software programming in Matlab and C

The work can be done by 1-2 persons, in the Malmö or Berlin office.

To apply, contact mohamad.abounasa@u-blox.com or matthias.mahlig@u-blox.com.

Project 2: Next Generation Wi-Fi connectivity and positioning

The aim of this thesis is to study next generation wireless techniques based on recent advancements of Wi-Fi technology (e.g., IEEE 802.11ax, 802.11az, 802.11be, 802.11bf) that make use of higher frequencies and much large bandwidths to advance connectivity.

With these enablers, the position estimation accuracy can be enhanced when combined with sophisticated algorithms and localization techniques.

Candidate profile:

  • Strong background in digital communications and wireless air interfaces (e.g. Wi-Fi, BLE)
  • Familiar with digital receiver design and signal processing techniques
  • Knowledge of wireless positioning methods (e.g. trilateration, angle-of-arrival (AoA), round-trip time (RTT))
  • Very good knowledge of software programming in Matlab (preferably), C or Python

The work can be done by 1-2 persons in the Malmö or Athens office.

To apply, contact peter.karlsson@u-blox.com or stelios.papaharalabos@u-blox.com

Project 3: IoT modules with embedded machine learning inference

There is a growing interest in tiny and embedded machine learning inference models for IoT applications. One typical use case is the reception and transmission of sensor data based on Thread and Wi-Fi protocols. Native IP is fully supported in both Thread and Wi-Fi, where IPv6 provides the space needed for directly addressing all IoT nodes and devices.

This thesis will investigate embedded machine learning models and inference for energy optimized IoT protocols and transmission schemes. The study will analyze how data communication intervals, packet sizes and real time requirements impact the energy consumption among network nodes. The study will focus on finding features and use machine learning (ML) in addition to the standardized PHY and MAC protocols. The goal is to have a compact ML model and embedded inference of sensor data schemes for low energy consumption and sustainable IoT modules.

Candidate profile:

  • Background in wireless digital communications (Wi-Fi, Thread)
  • Familiar with digital signal processing techniques
  • Knowledge of machine learning tools
  • Very good knowledge of software C and Python

The work can be done by 1-2 persons in the Malmö office.

To apply, contact peter.karlsson@u-blox.com

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Angle of Arrival

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Durgaprasad's article

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