Be you together with us.
SICK is a world-leading supplier of sensors and sensor solutions for industrial applications founded in 1946 by Dr. E.h. Erwin Sick. We are more than 12 000 employees in 50 countries and our headquarter is located in Freiburg, Germany.
SICK in Linköping is an innovation center for Machine Vision, and we are 90 committed employees with a big interest in image processing and visualization. For more than 35 years, our team at SICK Linköping has successfully developed and delivered software for technically leading products within the field of 2D and 3D vision, as well as system solutions, e.g. robot guidance and quality control.
We all have one thing in common. When something triggers our minds, it does so fully and completely.
For us, the only way to succeed is through genuine trust!
We celebrate each other’s individuality, curiosity, and desire to innovate.
At SICK Linköping we love our work, but life happens, and that is just the way it should be!
There are many opportunities for students at SICK Linköping. Join us for an internship or summer job. Write your thesis with us or kick start your career by combining your studies with part-time work.
Stay curious and keep learning. We give you the tools to succeed!
Let some of our employees tell you what they do at work and why they love to work at SICK Linköping
I started my career at SICK during the autumn of 2014 as an application developer. A role that gave me the opportunity to travel a lot and visit customers all around the world, something I found creative, varied and fun! Today I work as a product manager in the 3D vision team in Linköping. The opportunity to be involved in driving technological development in society forward, is something that I really appreciate about my job!
During my years at SICK I have also had the opportunity to work as a vision expert at one of SICK's sales subsidaries in the USA. A fun job that gave me a large network I can benefit from in my role today. I would describe SICK as a workplace with state-of-the-art technology and friendly colleagues, a workplace with great opportunities to develop and work abroad!
I started my career at SICK through a summer job in 2017, which went on to a part-time job alongside my studies. At the end of my studies, I wrote my master thesis at SICK and after that I started as an full-time application engineer. Today I work as a software developer, where I build applications that make it easy for customers to use our smart cameras.
The opportunity to make technology available and usable for many people, and not just the few knowledgeable experts, is something that I really appreciate about my job! During my years at SICK I have gotten the opportunity to grow and take on new challenges. I would say that it is the culture at SICK that has gained me the confidence to take on new challenges!
The helpful mindset at SICK was something I noticed already when I had my summer job here. No matter who I asked, I always got help, either an answer to my question or help to find the right person to ask. My colleagues at SICK Linköping are the best part of every work day!
For many years, we have been elected as one of the best workplaces in Sweden according to the survey Great Place to Work. We take great pride in our workplace culture characterized by helpfulness, lack of prestige, and a great passion for innovation. We have a strong team spirit, a friendly and open working climate, and great comradeship. We like to do things together, like maintaining our traditions, arranging an after-work, meeting up for some board games over lunch, or just engaging in an interesting conversation by the coffee machine.
The way we work at SICK Linköping is of an agile nature. We work in self-governing and cross-functional teams where you are given freedom with responsibility. We are convinced that the best ideas surface when we have open minds and help each other, so there is no competition at SICK Linköping, only collaboration. And because nobody knows everything, we share decision-making and direction setting. We solve problems together and celebrate together!
Taking care of our health, and engaging in proactive and health-promoting activities are important for us. We offer regular health examinations, health insurance, and visits to our masseur. We are many at SICK Linköping who like to activate ourselves. It is always possible to find a colleague who is up for an activity during lunch, maybe walking, running, mountain bikeing, or cross-country skiing on the nearby golf course during wintertime. We also play badminton and floorball together every week.
We love to learn and find new ways to develop our competence. For us, competence development is everything from traditional training, book circles, paper clubs to networking, and inviting each other to demos and Tech talks (our version of Ted talks). We also set aside time to make use of and trigger the curiosity and desire to try new ideas. We call these “Exploration days”, days when we explore new things alone, in groups, or together with all employees at SICK Linköping.
For us, Corporate Social Responsibility (CSR) is a valuable way to create additional meaning in our work. We do not want lack of time to be an obstacle to donating blood. At SICK Linköping you can therefore donate blood during working hours. We are very proud of our life-saving heroes! It is also up to our employees to decide on which organizations will receive our yearly sponsoring and charity donation. Do you also want to donate blood? Click here
We think it should be easy to choose the bicycle as a means of transportation when commuting to work. We have therefore ensured that we have access to locked bicycle storage, shower facilities, a drying room for wet clothes, and a repair kit for punctures at the office. As a result of our efforts, we have received the "Bicycle-friendly workplace" award. We measure how we commute to work, and make sure to celebrate when we achieve a new environmentally friendly commuting record!
For us sustainability is everything from creating products that help our customers optimize their use of the world’s resources to keeping a good work life balance. It is striving towards the goals of agenda 2030 while also having the opportunity to learn and develop professionally. In short it means keeping in balance with all elements of life.
Want to know more about our sustainability work within SICK group?
We are always looking for people and we want to know who you are! The first step is to apply!
What the Master Thesis is about/background to the problem to investigate
Vision Language Action models (VLA) show impressive results on many robotics tasks like picking and placing clothes. However, training a VLA requires huge amount of robot demonstrations and even fine tuning the action decoder step for a new robot requires a lot of demonstrations.
Since collecting real robot demonstrations is highly time-consuming, reducing the number of required demonstrations is highly beneficial. This can be achieved in several ways, for example through the use of simulation environments. This thesis investigates methods for reducing the need for real robot demonstrations.
The master thesis work focuses on the following/example of research questions
Prerequisites
You should have some familiarity with modern computer vision architectures and be motivated to dive deeper. You should be comfortable programming in Python.
Contact
For more information about the position, contact:
Anders Moe, Software Developer, anders.moe@sick.se
We warmly welcome your application — please submit it no later than October 25th.
SICK is a world-leading supplier of sensors and sensor solutions for industrial applications. We’re part of SICK AG — a global leader in sensor technology with 10,000 employees across 50 countries and headquarters in Freiburg, Germany. Together, we build technology that makes industries more efficient, intelligent, and safe.
As a Machine Vision Innovation Center, SICK Linköping develops advanced AI-powered software that drives the future of both manufacturing and logistics automation. Whether it’s helping robots pick the right item or enabling high-precision quality control with 2D and 3D vision, our solutions bring clarity, speed, and smart decision-making to complex industrial environments — all driven by a dedicated team of 100 colleagues.
We are very proud of being a healthy and attractive workplace. We have consistently been recognized as one of the best workplaces in Sweden according to the Great Place to Work survey. We actively work to reduce our climate footprint and engage in various initiatives to contribute to society and enhance diversity at our workplace.
What the Master Thesis is about/background to the problem to investigate
In industrial settings, one would often like to predict not only a bounding box or segmentation for an object visible in the image, but also determine exactly how the object is oriented and where its key points are located. This is especially relevant for objects that consist of well-defined parts but where the size and configuration of these parts can vary.
Modern vision architectures such as DETR or DEIM are built on shared backbones which can be combined with different heads for tasks like bounding box prediction, segmentation mask prediction or human pose estimation. This thesis would investigate how such architectures can be adapted to solve the “industrial pose estimation” task described above, with focus on situations that have only a few training images, so called few-shot problems.
The master thesis work focuses on the following/example of research questions
Prerequisites
You should have some familiarity with modern computer vision architectures and be motivated to dive deeper. You should be comfortable programming in Python. 
Contact
For more information about the position, contact:
Erik Hedberg, Senior Algorithm Developer, + 46 722 26 77 57
We warmly welcome your application — please submit it no later than October 25th.
SICK is a world-leading supplier of sensors and sensor solutions for industrial applications. We’re part of SICK AG — a global leader in sensor technology with 10,000 employees across 50 countries and headquarters in Freiburg, Germany. Together, we build technology that makes industries more efficient, intelligent, and safe.
As a Machine Vision Innovation Center, SICK Linköping develops advanced AI-powered software that drives the future of both manufacturing and logistics automation. Whether it’s helping robots pick the right item or enabling high-precision quality control with 2D and 3D vision, our solutions bring clarity, speed, and smart decision-making to complex industrial environments — all driven by a dedicated team of 100 colleagues.
We are very proud of being a healthy and attractive workplace. We have consistently been recognized as one of the best workplaces in Sweden according to the Great Place to Work survey. We actively work to reduce our climate footprint and engage in various initiatives to contribute to society and enhance diversity at our workplace.
What the Master Thesis is about/background to the problem to investigate
Industrial software is often evaluated based on technical performance and functionality. While these aspects are important, successful products must also be easy to learn, efficient to use, and straightforward to demonstrate and explain to customers.
Within industrial robot guidance, different stakeholder groups interact with software in different ways. Application engineers and integrators configure and deploy systems, operators use them in daily production, and sales engineers demonstrate their capabilities to prospective customers. Each group has its own expectations, challenges, and measures of success.
As industrial software becomes increasingly focused on usability and user experience, there is a growing need for methods that can evaluate products from a human perspective in addition to a purely technical one. Factors such as task completion time, error rates, intuitive workflows, cognitive load, and overall user satisfaction may provide valuable insights into how effectively a product supports its users.
This thesis will investigate how usability and cognitive load can be evaluated in industrial robot guidance software. The work may include comparing different software products through representative application setup tasks and identifying factors that influence both user experience and perceived product value.
The master thesis work focuses on the following/example of research questions
Prerequisites
This thesis will involve user studies, data analysis, experimentation, software engineering, and human-computer interaction.
You should have a strong interest in usability, user experience, software development, psychology, human-computer interaction, or industrial systems. Experience with experimental design, statistics, or user-centered design is beneficial.
Contact
For more information about the position, contact:
Fredrik Lindgren, Software Developer, +46 722 26 77 33, fredrik.lindgren@sick.se
We warmly welcome your application — please submit it no later than October 25th.
SICK is a world-leading supplier of sensors and sensor solutions for industrial applications. We’re part of SICK AG — a global leader in sensor technology with 10,000 employees across 50 countries and headquarters in Freiburg, Germany. Together, we build technology that makes industries more efficient, intelligent, and safe.
As a Machine Vision Innovation Center, SICK Linköping develops advanced AI-powered software that drives the future of both manufacturing and logistics automation. Whether it’s helping robots pick the right item or enabling high-precision quality control with 2D and 3D vision, our solutions bring clarity, speed, and smart decision-making to complex industrial environments — all driven by a dedicated team of 100 colleagues.
We are very proud of being a healthy and attractive workplace. We have consistently been recognized as one of the best workplaces in Sweden according to the Great Place to Work survey. We actively work to reduce our climate footprint and engage in various initiatives to contribute to society and enhance diversity at our workplace.
What the Master Thesis is about/background to the problem to investigate
Automated palletizing systems must continuously decide where and how objects should be placed on a pallet. The quality of these decisions has a direct impact on pallet fill ratio, throughput, and overall system efficiency.
Many packing and palletizing algorithms have been proposed, ranging from simple heuristics to advanced optimization techniques. While these approaches can perform well under certain conditions, their suitability depends on factors such as the available knowledge about incoming objects, computational requirements, and the constraints of real-world robotic systems.
In practical applications, decisions often need to be made with incomplete information and under changing conditions. Furthermore, solutions that perform well in simulation may not always translate directly to industrial environments where positioning errors, object variations, and other uncertainties are present.
This thesis will investigate optimization of placement strategies for vision-guided robotic palletizing. The work may include studying existing palletizing algorithms, evaluating their strengths and limitations, and exploring how information about current and future objects can be used to improve palletizing performance.
The master thesis work focuses on the following/example of research questions
Prerequisites
This thesis will involve programming, algorithm development, mathematics, optimization, and computer vision. You should have a strong interest in problem solving and software development. Experience within optimization, computer vision, robotics, or related fields is beneficial.
Contact
For more information about the position, contact:
Fredrik Lindgren, Software Developer, +46 722 26 77 33, fredrik.lindgren@sick.se
Anders Moe, Algorithm Developer, anders.moe@sick.se
We warmly welcome your application — please submit it no later than October 25th.
SICK is a world-leading supplier of sensors and sensor solutions for industrial applications. We’re part of SICK AG — a global leader in sensor technology with 10,000 employees across 50 countries and headquarters in Freiburg, Germany. Together, we build technology that makes industries more efficient, intelligent, and safe.
As a Machine Vision Innovation Center, SICK Linköping develops advanced AI-powered software that drives the future of both manufacturing and logistics automation. Whether it’s helping robots pick the right item or enabling high-precision quality control with 2D and 3D vision, our solutions bring clarity, speed, and smart decision-making to complex industrial environments — all driven by a dedicated team of 100 colleagues.
We are very proud of being a healthy and attractive workplace. We have consistently been recognized as one of the best workplaces in Sweden according to the Great Place to Work survey. We actively work to reduce our climate footprint and engage in various initiatives to contribute to society and enhance diversity at our workplace.
Background
SICK’s laser triangulation cameras produce spatially aligned 2D grayscale images and 2.5D heightmaps. Together, these two modalities provide complementary information about an object: grayscale images capture visual appearance and texture, while heightmaps provide information about surface geometry.
Heightmap images are by nature much dissimilar to 2D grayscale images: each pixel represents its height and may even be undefined. Due to this, existing AI vision encoder backbones -- often only trained on natural images -- are not able to represent heightmaps in a good way.
Thesis objective
JEPA (Joint-Embedding Predictive Architecture) is a self-supervised learning framework receiving much attention from the scientific research community. For missing information, JEPA-style methods learn to recover the missing information from its latent space, rather than first reconstructing pixels. This makes them an interesting candidate for learning representations that are robust to occlusions and missing height data.
The plan is to fine-tune an existing JEPA-trained backbone with aligned 2D/2.5D images, teaching the model to place the latent representations of the height map images close to its 2D counterpart. We will then compare the performance of such fine-tuned models on specific downstream tasks, for different amounts of missing data.
Research environment
The thesis will be carried out at SICK, in collaboration with the Computer Vision Laboratory (CVL) at Linköping University.
You will work closely with Matheus Bernat, an industrial PhD student at SICK and CVL, Linköping University, whose research focuses on multimodal AI, and representation learning.
The project is intended to have a strong research component. For a motivated student, there is an opportunity to work towards a joint publication towards the end of the thesis.
Contact
For more information about the position, contact:
Matheus Bernat, matheus.bernat@liu.se
We warmly welcome your application — please submit it no later than October 25th.
SICK is a world-leading supplier of sensors and sensor solutions for industrial applications. We’re part of SICK AG — a global leader in sensor technology with 10,000 employees across 50 countries and headquarters in Freiburg, Germany. Together, we build technology that makes industries more efficient, intelligent, and safe.
As a Machine Vision Innovation Center, SICK Linköping develops advanced AI-powered software that drives the future of both manufacturing and logistics automation. Whether it’s helping robots pick the right item or enabling high-precision quality control with 2D and 3D vision, our solutions bring clarity, speed, and smart decision-making to complex industrial environments — all driven by a dedicated team of 100 colleagues.
We are very proud of being a healthy and attractive workplace. We have consistently been recognized as one of the best workplaces in Sweden according to the Great Place to Work survey. We actively work to reduce our climate footprint and engage in various initiatives to contribute to society and enhance diversity at our workplace.
What the Master Thesis is about/background to the problem to investigate
Modern machine vision systems continuously increase in resolution and frame rate, creating demanding requirements on data transfer performance between cameras, processing units, and host computers. While GigE Vision is widely adopted in industrial vision applications, emerging technologies such as RDMA over Converged Ethernet (RoCEv2), USB4, and Thunderbolt offer new opportunities for achieving higher throughput and lower latency with reduced CPU overhead. This thesis aims to investigate and compare state-of-the-art high-speed communication technologies suitable for future machine vision products, with particular focus on FPGA-based implementations and RDMA technologies.
The master thesis work focuses on the following/example of research questions
The thesis will be carried out at SICK in Linköping. You will work in a team developing next-generation machine vision solutions and gain hands-on experience with advanced networking technologies, FPGA platforms, Linux-based embedded systems, and high-performance data acquisition architectures.
FPGA, RDMA, RoCEv2, GigE Vision, High-Speed Networking, Zynq UltraScale+, Embedded Linux, Hardware Acceleration, Ethernet, Machine Vision.
Prerequisites
Contact
For more information about the position, contact:
Fredrik Claesson, fredrik.claesson@sick.se
We warmly welcome your application — please submit it no later than October 25th.
SICK is a world-leading supplier of sensors and sensor solutions for industrial applications. We’re part of SICK AG — a global leader in sensor technology with 10,000 employees across 50 countries and headquarters in Freiburg, Germany. Together, we build technology that makes industries more efficient, intelligent, and safe.
As a Machine Vision Innovation Center, SICK Linköping develops advanced AI-powered software that drives the future of both manufacturing and logistics automation. Whether it’s helping robots pick the right item or enabling high-precision quality control with 2D and 3D vision, our solutions bring clarity, speed, and smart decision-making to complex industrial environments — all driven by a dedicated team of 100 colleagues.
We are very proud of being a healthy and attractive workplace. We have consistently been recognized as one of the best workplaces in Sweden according to the Great Place to Work survey. We actively work to reduce our climate footprint and engage in various initiatives to contribute to society and enhance diversity at our workplace.
Develop high-performance algorithms for embedded vision systems using C++. Join an innovative team transforming research into real-world products, with a focus on edge AI, computer vision, and advanced industrial imaging applications.
You can always let us know who you are and what you are interested in!
We don’t want to brag, but here are some stats for you!
We are the People and Culture team at SICK Linköping. We can answer all your question on recruitment, life at SICK Linköping, student opportunities and much more.
Sarah Lantz
sarah.lantz@sick.se
+46 739 10 99 37
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