Master thesis proposals - external

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

SCANIA Smart Factory Lab

Scania’s Global Industrial Development has a Smart Factory Lab Team where new techniques are adapted, evaluated, demonstrated and implemented. Within this area we have a number of possible topic proposals:  

• AI for Human Well-Being ( Human Pose Estimation / Motion Capture)  
• AI for Computer vision (Generate synthetics training data for production/Object detection for quality inspection) 
• Two autonomous mobile robots exchange material 

• Modern navigation and planning of mobile robots (with ROS) 
• AI for improving Energy Consumption and System Performance in Production 

We are looking for a number of students to collaborate with us. Do you have another idea? Projects may also be initiated by you, so we welcome suggestions. Please indicate your interest area in the cover letter.  

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

More details and Apple here: 30 credits – Smart Factory Lab - Machine Learning and Robotics for Future Industrial Digitalization and Automation

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 Infinitiy

Ocean Infinity has a number of proposals  for thesis projects during Fall, starting in the end of August 2022.  These involve problems within underwater perception, sonar and robotics.  For example:

• Target Identification and classification from sidescan sonar  data.
• Marine Growth assessment using computer vision, (Marine Growth Classification)
• MultiBeam Echo Sounder (MBES) image alignment (Multibeam Sonar Image Alignment)
• Image quality assessment (visibility, exposure, lighting, positioning etc) (Automated Image Assessment)
• 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

ABB Corporate Research

Using dialogue to disambiguate robot manipulation, learning from demonstration.

Behavior Trees are a reactive task switching policy representation, used to control robotic agents. LfD can be used to teach the robot a task and BTs can be generated out of human demonstrations. However, ambiguities might rise if the target object for the task is similar to other objects in the environment. Thus, verbal-HRI can be used to disambiguate the task, both during the learning step and the execution step. The objective of the project is then to build a framework for continuous interaction between human and robot, from learning a task to execute it.

Start: between Jan. 2022 and June 2022
Duration: 6 months
Place: ABB CRC (Västerås)
ABB will cover the accommodation in Västerås
Contact: Matteo Iovino, matteo.Iovino@se.abb.com

Details here.

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