Master thesis proposals - external

SkyCraft AB Master projects in CV and ML on powerline detection

Description

Viscando AB CV and ML diploma projects at ViscandoProjects within deep learning, signal processing and modelling for traffic and autonomous vehicle safety

Description

Arkus AI Apply Machine Learning and Computer Vision in Genetic Diagnostics

Arkus AI is a fast-growing AI (Artificial Intelligence) startup created in January 2020, as a result of an industry project co-founded by a Vinnova innovation grant for SME. Arkus AI is created with an ambition to innovate AI technologies in the field of genetic diagnostics and healthcare. Its vision is to deliver AI worldwide in the spirit of continuous innovation and earnest compassion. We are on track to deliver the first AI-powered automation tool in clinical genetics and we are delighted to offer the following two Master’s thesis projects.

Project I: Research and develop an innovative solution that combines both deep learning and computer vision technics to understand medical images from clinical genetics. For this project, the student must have an outstanding competency in computer vision, in addition to deep learning skills.

Project II: Innovate in deep learning technics in the search of a high performing classifier of processed medical images from clinical genetics. For this project, the student must be fluent with existing deep learning methods in the field.

Required competencies: Passionate for innovation, self-motivated, and can work independently; Competent in machine learning in particular deep learning; Fluent in Python programming language; Some industrial experience in software programming; Excellent in oral and written English.

Students will be mentored and challenged by Arkus’ exceptionally talented and compassionate engineers. Upon completion of the thesis, highly qualified students will be offered positions to walk the startup journey with us.

Location: Working remotely until Covid situation clears. Regular meet-ups at AI Sweden’s Stockholm office or THINGS at KTH. No requirement to be physically in Stockholm

Contact: join@arkus.ai or ying@arkus.ai, Ying Cheng/Co-Founder, M: 0760901386

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

Scania Autonomous Transport Solutions A thesis project at Scania is an excellent way of making contacts for your future working life. Many of our current employees started their career with a thesis project. The concept of autonomous driving has rapidly transitioned from being a futuristic vision of robotics and artificial intelligence to being a present-day reality. Scania is among the companies aiming to sell fully autonomous vehicles a few years from now, but many research challenges still needs to be solved.

Within the topics of robotics, perception and learning we have several interesting projects:

* Risk-aware Decision Making with POMDPs
* Online Estimation for of Safety Margins for Risk Assessments
* Behavior Prediction of Surrounding Cyclists for Autonomous Driving
* Collision checking for articulated buses
* Steering Methods for Nonholonomic Motion Planning
More projects may also be published at Scania Career.

Contact:See link for each project, or contact Truls Nyberg, Industrial PhD Student Scania/RPLtrulsny@kth.se

Reinforcement Learning at Flexible Alternating Current Transmission Systems You will be part of the Business Unit Grid Integration, located in Västerås. FACTS (Flexible Alternating Current Transmission Systems) technologies provide more power and control in existing AC as well as green-field networks and have minimal environmental impact. With a complete portfolio and in-house manufacturing of key components, ABB is a reliable partner in shaping the grid of the future. Please find out more about our world leading technology at www.abb.com/facts.

Problem description

At FACTS you will aid in creating a more sustainable future. The increase of available measurements, growth of real time processing capacity and communication abilities is changing the opportunities in the power system landscape. There is a potential to utilize more measurement data, Reinforcement Learning and a FACTS device for optimization purposes.

The task would include: • Create suitable test networks• Investigate and develop prototype algorithm based on latest advancements in deep Reinforcement Learning• Train, tune and test the algorithms on the networks and demonstrate policy optimality

Requirements

We are looking for you who are studying a university master program within a relevant technical area along with an interest within artificial intelligence or Reinforcement Learning. Experience in Reinforcement Learning or programming is positive. We aim to start the thesis in early September.