Several PhD. Scholarships in AI, Deep learning and other advanced disciplines for international students at Chalmers University/Study in Sweden
- Omran Aburayya
- May 8, 2024
- 1 min read
Updated: May 14, 2024
Chalmers University is inviting applicants to fill several PhD. scholarships for international students in different fields of AI and other advanced sciences. See below and click on each position of interest to visit the official post.
One PhD position that is placed at the Division of Interaction Design and Software Engineering, with Chalmers University of Technology as the employer. As part of your research project, you will (micro-)benchmark Java-based applications using JMH. You will collect performance measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning techniques (e.g., deep neural networks, graph neural networks, or active learning) with the goal of predicting the performance impact of code changes. A strong background in software engineering as well as some interest in machine learning will be required for this project. Interest in compilers and the Java Virtual Machine are a benefit to this project.
Application Deadline: 07/06/2024
One PhD position that is placed at the Division of Interaction Design and Software Engineering, with Chalmers University of Technology as the employer. The position requires a Master's level degree, corresponding to at least 240 higher education credits in, for instance, production engineering, industrial engineering, human-computer interaction, automation, education, cognitive sciences, or similar.
Your Master thesis project should have corresponded to at least six months of studies. Courses and Master thesis projects in your CV should normally be in the areas mentioned above. Experience in areas of relevance for the position is important. We normally collaborate closely with companies, so work experience from industry and multi-stakeholder interaction is highly valued.
Application Deadline: 2024-05-31
We are looking for a highly motivated PhD student that wants to pursue forefront experimental research on ferroelectric nematic liquid crystals, with the aim to further increase the knowledge about the mechanisms behind their formation and their phase behavior. From an applicational point of view, the understanding of the interplay between NF materials and bounding surfaces, is of special importance. Depending on the progress and evolution of the research activities, the work could also involve exploring ferroelectric nematic structures for energy harvesting and storage applications. The hired PhD student is encouraged to, and will have the possibility, to also bring in his/her own research ideas into the work.
Application deadline: 2024-06-30
The selected candidate will be embedded into an interdisciplinary environment between the Chalmers departments of Computer Science and Engineering as well as Chemistry and Chemical engineering, and the Wallenberg Centre for Quantum Technology (WACQT). This inter-disciplinary research project is part of an on-going collaboration between the groups of Simon Olsson, Anton Frisk Kockum, and Martin Rahm to enable quantum chemistry on quantum computers aided by machine learning. The selected candidate will be based at the Computer Science and Engineering department yet enjoy full access to and a work-place at WACQT and the Chemistry and Chemical Engineering departments.
Application deadline: 2024-05-31
Project description
Climate change mitigation, energy security concerns and cumulative technical development have led to a renewed interest in nuclear energy. However, established nuclear fission technology suffers from long construction times, high up-front costs, limited learning, and high political risk. Small modular reactors (SMRs) could potentially open a new trajectory for nuclear fission, and thermonuclear fusion could open a completely new space for innovation and industrial development. To realise such visions, not only technical problems, but also a broad set of economic and institutional barriers related to developing new industrial value chains need to be overcome. This PhD project at Chalmers, in cooperation with RISE, applies the technological innovation systems (TIS) framework to identify barriers and drivers in different parts of the emerging sociotechnical systems around SMRs and fusion reactors, with the aim of providing foresight to policymakers and industry.
Application deadline: 2024-05-26
This PhD project will explore this novel amyloid activity in vitro, in cells, and through metabolomic approaches. The focus will be on alpha-synuclein and Parkinson, but other amyloidogenic proteins may be included. In addition, the role of metal ions as modulators of catalytic activity, liquid-liquid phase separation (LLPS), and final amyloid structure will be elucidated. The project will involve a range of biochemistry and biophysics (spectroscopy, microscopy) methods using purified proteins along with cell culture studies as appropriate. Some parts of the project may involve collaborators.
The project may change focus over time, depending on results and interests. Overall, the goal is to reveal new knowledge about amyloids that, in the long-term, is helpful to combat neurodegenerative disorders.
Application deadline: 2024-05-31
We are seeking a highly motivated and talented Ph.D. researcher to join our team and contribute to an exciting research project focused on optical quantum network technologies. By leveraging the principles of quantum mechanics, the successful candidate will explore innovative ways to seamlessly integrate quantum computing and quantum communication protocols into classical optical infrastructure. This endeavor holds immense potential to revolutionize information processing and communication paradigms.
Application deadline: 2024-05-15
A shift toward production and consumption of foods with lower environmental impact and better use of our food side streams is necessary. This PhD position addresses these challenges and aims to develop innovative routes for valorization of different food processing side streams and their use in development of hybrid plant-based food products.
The PhD-student will be enrolled at the Chalmers Bioscience Graduate School, and most experimental work will be performed at Chalmers. However, visits to the collaborating university and industry partners are foreseen.
Application deadline: 2024-05-26
For this project, the PhD candidate will work on a novel, multi-modal deep learning approach can be used to predict the aforementioned properties, wherever data is readily available, thus setting the stage for the de novo design of non-toxic, PFAS replacement materials with
low environmental impact. Our proposed approach integrates molecular dynamics with experimental data to learn meaningful representations of the materials. We will highlight its utility on the prediction of PFAS toxicity, chemical and thermal stability, and degradation pathways. These properties are keenly dependent on both the molecular and crystal structures of the material.
This project is part of a collaboration between the AI Laboratory for Biomolecular Engineering, led by Dr. Rocío Mercado at Chalmers, and the Intel-Merck AWASES Program, a joint academic research center between Intel and Merck with the goal of accelerating sustainable semiconductor manufacturing processes.
Application deadline: 2024-05-25
This PhD position offers an opportunity to delve into the area of deep generative modeling within the broader landscape of machine learning, computer vision and medical image analysis. As a candidate, you'll have the chance to develop theoretical concepts and innovative methodologies while contributing to real-world medical imaging applications. Moreover, you will enjoy working in a diverse, collaborative, supportive and internationally recognized environment.
PhD project centers on deep generative models for image synthesis, creating realistic images from limited data, and image translation, transforming images between different domains. We aim to explore the application of these models in various medical image analysis areas, including domain adaptation, weakly supervised segmentation, out-of-distribution detection and model explainability. The goal is to develop state-of-the-art methods based on key enabling techniques from machine learning such as representation disentanglement and conditional generative networks.
Application deadline: 2024-05-20
The lithium-ion battery is the dominant rechargeable energy storage devices. In order produce high-performance lithium ion battery, different component need to be optimized, especially the binder which plays a critical role not only in the mechanical properties of the formed electrode but also in determining the long-term cycling performance of the battery. However, the polymer-based binders used for both anode (especially silicon-based anode) and cathode have their issues. This PhD position will focus on the development of a new binder system, which includes both the synthesis/modification of the biomaterials-related binders and also development of a new electrode manufacturing process.
Application deadline: 2024-05-31
Generative AI, with tools like ChatGPT, driven by Large Language Models (LLMs), have in the past year demonstrated impressive capabilities in generation of not only fluent text but also computer code. They take instructions in natural language and can perform seemingly creative tasks very well. However, LLMs can be unreliable: a well-known issue is hallucinations where seemingly sensible but incorrect answers are produced, and their reasoning capabilities are fragile.
Research mathematicians are increasingly staring to use computers to assist in their research, not least in cases where proofs turn out to be so large that it is impossible to check each step manually (a amous example is Thomas Hales' proof of Kepler's conjecture). Proof-assistants are software designed for exactly this: keeping track of precise definitions and checking each step of the proof. However, using a proof assistant requires both expertise in how to program them, and libraries of foundational mathematics to already be in place.
We believe that LLMs has the potential to speed up this process and even provide creative suggestions to working mathematicians. But not on their own: Proof assistants have the complementary capabilities missing for this to work. The goal of this proposal is therefore to bring LLMs and proof assistants together via a neuro-symbolic architecture.
Application deadline: 2024-05-31
The development of new acceptor materials for organic solar cells has led to a steady increase in device efficiencies, reaching more than 17% today. A remaining challenge is the relatively poor long-term stability of organic solar cells. The goal of your research will be to understand how the use of mixtures of acceptor molecules influences and improves the stability of organic solar cells.
Application deadline: 31 May, 2024
The position is in the field of data-driven epidemiology and biology of infection, which covers research that will transform our understanding of pathogens, their interactions with hosts and the environment, and how they are transmitted through populations. The research in this field have a strong focus on computational analysis or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or it may use population-scale genetic, clinical, or public health data from pathogen surveillance efforts and biobanks.
The position is interdisciplinary and combines mathematics and computer science with applications in biology in medicine. A substantial part of the research will therefore be done in close collaboration with scientists from different disciplines. The Ph.D. position will be supervised by Professor Erik Kristiansson and will be part of a research group combining multiple competencies. The position will be co-supervised by Professor Joakim Larsson at the Department of Infectious Diseases, University of Gothenburg, Johan Bengtsson-Palme, Chalmers University of Technology, and Dr. Anna Johnning, Fraunhofer-Chalmers Centre.
Application deadline: 2024-06-07
Together with the successful applicant, we aim to consider how to combine techniques rooted in first principles (time-to-collision, physics) and symbolic approaches developed in software engineering (e.g., formal verification, testing, synthesis) with the engineering of learning-based software components (e.g., DNN) to ensure that the learning-enabled component will not be the source of harm. In the past, we have developed approaches to formally verify and test autonomous systems, designed specialized loss functions reflecting the safety principles, and developed runtime verification techniques for abnormality detection. The results have been applied in concrete industrial use cases such as autonomous driving, intelligent defect inspection for high-speed trains, and factory automation.
This position revolves around the fundamental principles of AI and machine learning, with a specific emphasis on reinforcement learning. Machine learning encompasses a range of data-driven techniques that derive models for tasks such as prediction, exploratory data analysis, and explanation. The primary focus of this position is on reinforcement learning which examines the interactions between an agent and its environment, aiming to maximize cumulative rewards (to reach the goal as efficient as possible). Within this project, we will develop novel reinforcement learning methods considering aspects such as sample efficiency, robustness, efficient training and inference, multi-agent reinforcement learning, etc. This position will explore fascinating theories and innovative methods while also giving you the chance to investigate them in interesting real-world applications.
Application deadline: 2024-06-10
