Master thesis proposals - external

Ericsson Research The Machine Learning team in Ericsson Research now has a position on safe learning-based control for telecommunication networks! If exploring intelligent control strategies that maximise performance while ensuring safety is something that sounds interesting, please do not hesitate to apply by May 9th.¶

Master Thesis - Balancing Safety and Performance: Intelligent Control Strategies for Learning-Based Systems¶

Contact person: Ioanna Mitsioni ioanna.mitsioni@ericsson.com ¶

ABB Corporate Research Center ABB Corporate Research Center is offering a position for generating behavior tree policies for robots by combining planning and learning together with a user interface for human input and feedback. Please apply by 18th of December!

https://careers.abb/global/en/job/89031397/Thesis-Work-for-Behavior-Tree-Policy-Editor

Marcus Wallenberg Laboratory for Sound and Vibration Research A project in applying LLMs to literature search:

LLM_Project_MWL

ABB Robotics ABB Robotics are offering two positions in the motion control group for spring 2024 for exploring the intersection of AI/ML and robotics. Please apply by 13th of November!

https://careers.abb/global/en/job/89028627/Thesis-Work-Robotics-R-D-Motion-Control-generative-AI

https://careers.abb/global/en/job/88926384/Thesis-Work-Robotics-R-D-Motion-Control-machine-learning

Scania Autonomous Driving The concept of autonomous driving has rapidly transitioned from being a futuristic vision of robotics and artificial intelligence to being a present-day reality. We are looking for thesis students starting from January 2024. Please see our open projects below, welcome to apply!

Thesis Project - 30 credits – Motion Prediction and Planning with Self Supervised Learning for Autonomous Driving

Contact person: Yi Yang (yiya@kth.se) / John Folkesson (johnf@kth.se)

Thesis Project - 30 hp – Master's Thesis: LiDARs Images Fusion for Object DetectionThesis Project - 30 hp – Master's Thesis: Road Surface Estimation based on Multi-Sensory DataThesis Project - 30 hp – Master's Thesis: Dynamic Occupancy Grid with LiDAR

SCANIA Production & Logistics In the age of Industry 4.0, automation and digitalization have emerged as transformative forces reshaping the manufacturing landscape. Scania’s Global Industrial Development is responsible for developing and maintaining Scania’s common core competence within Production & Logistics. We are looking for thesis students starting from January 2024. Please see our open projects below, welcome to apply!

Thesis Project - 30 credits – Future Manufacturing Quality Inspection System with Deep Learning-Based Computer Vision and Synthetic Data

Thesis Project - 30 credits – Using AI for Energy Efficiency Analysis in ManufacturingThesis Project 30 credits – Smart Factory Lab - Robotics & Automated Cable Assembly, Computer vision for safety & Generative AI for Operator support

Contact person: Xiaomeng Zhu (xiaomeng.zhu@scania.com)

Silo AI Silo AI is Europe's largest private AI lab. We partner with industry leaders to build smart devices, autonomous vehicles, Industry 4.0, and smart cities. Silo AI recently launched SiloGen, which is now part of a groundbreaking consortium aimed at building the world's largest open-source Large Language Model. With SiloGen, our specialized arm in generative AI, we are redefining the future of AI and ensuring European digital sovereignty.

This spring we offer two exciting Master projects on Generative AI. Please follow the instructions in the descriptions below on how to apply. Welcome with your application!

Adaptive Generative Agents in Dynamic Environments

Stable Diffusion for Spatio-Temporal Consistent Generations

Ericsson Research Looking for a thesis project during Spring, starting in January 2024? Did you know Ericsson Research develops research on themes related to robotics and AI? Take a look at the current projects we have open:

Ericsson Research - Short MSc Thesis Description - Spring 2024

Requirements

All the projects require some common skills:

* Strong ability to formulate problems and solve them, independently and in groups.
* Programming skills, preferably in Python or C/C++.
* Strong communication skills in written and spoken English.
To apply or get more information

Project 1: Fernando dos Santos Barbosa - fernando.dos.santos.barbosa@ericsson.com

Hitachi Energy The electrical power system is evolving due to e.g. an increasing amount of renewables and distributed power generation. FACTS (Flexible AC Transmission Systems) devices can assist with this evolution by improving the power quality and reducing the risk of system disconnections.

In this master thesis proposal, we want to investigate the application of machine learning to a FACTS device. The device itself is based on a power electronic converter and has its own local controller. The performance, in terms of efficiency and stability, is dependent on modelling assumptions which may to a certain extent deviate from reality. Here, the objective would be to improve the performance of the FACTS device, by application and integration of a suitable machine learning method into the control. The master thesis is planned to start in January 2023.

Contact: Jonathan Hanning, jonathan.hanning@hitachienergy.com

For more details, see: https://www.hitachienergy.com/career/jobs/details/SE53908798_E1

Ocean Infinity Ocean Infinity has a number of proposals for thesis projects. These involve problems within underwater perception, sonar and robotics. For example:

* Target Identification and classification from sidescan sonar data.
* MultiBeam Echo Sounder (MBES) image alignment and outlier rejection.
* Localization of AUV swarm (SLAM loop closing)
Please contact John Folkessn johnf@kth.se for more information.

Arriver Sweden Knowledge Distillation for Autonomous Driving

Description

CNN-based association for Multiple Object Tracking

Description

Univrses Cross-city domain shift is the cause of a 25-30% metrics drop of normal Deep Learning predictors. Change of weather and lighting conditions might even result in more disruptive effects. Therefore, to provide a robust and accurate output, the predictor would need to be trained on every possible deployment condition, e.g. multiple cites, districts, seasons, kinds of weather, cameras pose and intrinsics. This largely reduces the scalability of AI solutions both in terms of costs and deployment time. Recent Unsupervised Domain Adaptation (UDA) methods proved to be very effective in mitigating (or even solving) these shortcomings.

Contact: Pier Luigi Dovesi <pier.luigi@univrses.com>Application link, full description and details: https://career.univrses.com/jobs/1401972-master-thesis-project-unsupervised-domain-adaptation

SkyCraft AB Master projects in CV and ML on powerline detection

Description

Viscando AB Projects within deep learning, signal processing and modelling for traffic and autonomous vehicle safety

Description

Babyshop Group Leveraging transfer learning for multi-label attribute prediction in fashion images, using deep learning

At Babyshop Group, there exist tens of thousands of images of children’s apparel. Currently, new items are labeled by hand, where each item has multiple attributes, eg. color, category, pattern, neckline, and style, etc. Since the workload of this manual task is immense, a need for a support system is high. In literature, a multitude of previous work has been done in regards to multi-label attribute prediction on fashion clothing, most notably DeepFashion and iMaterialist Fashion. Here, deep learning models have been trained on hundreds of thousands of labeled images to predict corresponding attributes, which has proven highly successful. However, almost all of these images are of adult fashion, with very little emphasis on childrens’ fashion. The question then arises, is it possible to combine the pre-trained models from previously mentioned multi-label attribution prediction models that are trained on almost exclusively adults’ fashion, together with the learnings from transfer-learning frameworks - in order to successfully perform multi-label attribution prediction on childrens’ fashion?

Required qualifications: MSc studies in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or related field; Good understanding of machine learning frameworks such as Keras, TensorFlow, PyTorch, Scikit-Learn, and/or Spark; Proficiency in Python and Git; Knowledge about data wrangling and data munging, using SQL, Pandas, and Numpy.

Web version of the proposal with instructions on how to apply.

Time frame: from early January to mid-June.

Contact: Marcus Svensson (marcus.svensson@babyshop.se), Data Scientist at Babyshop Group