Opportunities for Students

Your Potential. Our Future.

It is part of our success to develop young talents and to support your growth with our know-how. And it is 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 product 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: Wideband embedded antenna concepts

Antennas are critical components for all wireless communication technologies. The antenna performance is dependent on its size and structure as well as on the layout of the ground plane and objects in the vicinity of the antenna. When developing modules with internal antennas providing large bandwidth and good radiation characteristics, it is also important to consider how the modules are placed on the carrier board.

The goal of this topic is to study different antenna concepts and compare the performance for extended bandwidth covering the frequency bands for Wi-Fi and UWB in the frequency range from 5 GHz to 9GHz. The thesis work will include technical studies, calculations, design simulations and measurements of prototypes. The measurements will be made using different shapes of the carrier board. The analysis and documentation of simulation results and measurement data of prototype performance will be included in the thesis studies.

Candidate profile:

  • Background in wireless air interfaces (e.g. Wi-Fi, UWB)
  • Familiar with RF and antenna design
  • Good knowledge of RF simulation tools (e.g. HFSS)

We think this can be done by 1-2 persons.

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

Project 2: Hybrid wireless positioning

Demanding positioning use cases and scenarios are being envisaged lately, specifically when there is a need to navigate or track devices in a seamless manner between and within indoor and outdoor environments.Seamless positioning methods and fusion of inputs from different technologies must both enable the universal aspects and enhance the localization accuracy.

The goals of this thesis are to refine methods and fusion of GNSS and short-range wireless technologies for seamless indoor and outdoor positioning.Take part in measurements and collection of positioning data for analysis. The fusion algorithms in the core positioning engine will be evaluated both for network-based tracking and device-based navigation use cases. The study will evaluate fusion based on signal processing, also potential improvements in combination with AI and machine learning inference models.

Specific tasks in the thesis

  • Measurements and analysis of data from indoor and outdoor system
  • Evaluation of sensor fusion algorithms
  • Description and comparisons of hybrid positioning performance

Candidate profile:

  • Background in wireless positioning technologies(e.g.GNSS Bluetooth direction finding, UWB)
  • Good knowledge in signal processing and matlab tools
  • Familiar with machine learning

This thesis work can be done by 1-2 persons.

To apply, contact farshid.rezaei@u-blox.com

Project 3: 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

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

Project 4: IoT modules with embedded machine learning

There is a growing interest in short range radio technologies and protocols 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, Bluetooth)
  • 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 IoT, Thread, Wi-Fi, embedded ML,

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

Project 5: Embedded machine learning for anomaly and intrusion detection in IoT modules

There is growing interest to use wireless IoT modules for a wide variety of applications in industrial and consumer segments. The wireless IoT modules and connectivity between different nodes must be robust and secure.

This thesis will investigate how embedded machine learning models can be used to detect and mitigate anomalies in an IoT module and in a network topology of several nodes. The study will evaluate protocol stacks and data collected from nodes based on IoT modules in a larger network. The study will compare features and use machine learning (ML) based on collected data to investigate if anomalies and/or intrusion can be detected. The goal is to have a compact embedded ML model capable of real time inference of anomaly and intrusion in constrained Wi-Fi, Thread and Bluetooth IoT modules.

Candidate profile:

  • Background in wireless digital communications (Wi-Fi, Thread, Bluetooth)
  • Familiar with security and privacy
  • Very good knowledge of machine learning tools
  • Capable of coding and software tools C and Python

The work can be done by 1-2 persons security, privacy, embedded ML

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

Project 6: Indoor Positioning System based on adaptive machine learning

Indoor positioning and navigation have become an increasingly interesting application during recent years. There are many use cases visible in the market e.g.,user navigation on airports or in shopping malls, tracking of equipment in hospitals or goods in production plants. Different short-range technologies such as Bluetooth, WLAN and UWB can be utilized for such positioning systems.

One of the biggest challenges in indoor environments is signal degradation due to multipath propagation and attenuation. Machine Learning can significantly improve the accuracy and efficiency of these systems compared to classical estimation methods.

Specific tasks in the thesis:

  • Development of a positioning system based on Bluetooth and adaptive machine learning
  • Finding an adaptive machine learning model which is applicable for different indoor environments
  • Prototype implementation of the ML algorithms on an embedded system
  • System verification and performance tests

Candidate profile:

  • Extended knowledge in ML
  • Software development on embedded systems, Python, C
  • Basic knowledge in wireless communication technologies (Bluetooth)
  • Basic RF and antenna knowledge, Hardware test and measurements

The work can be done by 1-2 persons Indoor positioning, Bluetooth direction finding,embedded ML

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

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