WEDA

Topic: Autonomous Lagoon Cleaning Robot

Want to know more than the title? See https://docs.google.com/document/d/1yJgXx_67iAjXnp0dNsuYrqEIz_7W48xOXWyYLkrFQaI/edit?usp=sharing

Interested? Send your CV, some description of why your interest in this project is of extra large interest, and also some initial ideas for approaching this problem to PandionAI to johnf@kth.se.

PANDIONAI

Topic: Change Detection for AI Analysis in Remote Sensing Imagery

Are you passionate about staying on the cutting edge of Computer Vision, Artificial Intelligence, and Earth Observation from space? Join us at PandionAI for your Master's Thesis and embark on the development of our satellite constellation AlertSat.

The scope of the master thesis and introduction of the company can be found here: MASTER THESIS PROJECT AT PANDIONAI

Interested? Send your CV, some description of why your interest in this project is of extra large interest, and also some initial ideas for approaching this problem to PandionAI to career@pandionai.com ASAP or before the 10th of November.

We primarily look for students studying applied mathematics, physics, or Machine Learning, but any similar background is of interest!

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

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

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

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