Position:	PhD student in Generative Machine Learning & Computational Statistics
Institution:	Linköping University
Location:	Linköping, Östergötland County, Sweden
Duties:	We are looking for a PhD student working in the intersection of generative machine learning and computational statistical inference. Generative models based on diffusion processes have emerged as a prominent approach to machine learning with impressive performance in many application domains. A well-known use case is for image generation (these models are the main workhorse for tools such as DALL-E and Stable Diffusion) but the same technology has also shown great promise in applications as diverse as probabilistic weather forecasting, biochemistry, and materials discovery
Requirements:	You have graduated at Master’s level in machine learning, statistics, computer science, or a related area. A successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience
Text:	For full functionality of this page it is necessary to enable JavaScript. PhD student in Generative Machine Learning & Computational Statistics Linkoping Reference number IDA-2024-00089 We have the power of over 40,000 students and co-workers. Students who provide hope for the future. Co-workers who contribute to Linköping University meeting the challenges of the day. Our fundamental values rest on credibility, trust and security. By having the courage to think freely and innovate, our actions together, large and small, contribute to a better world. We look forward to receiving your application! Your work assignments We are looking for a PhD student working in the intersection of generative machine learning and computational statistical inference. Generative models based on diffusion processes have emerged as a prominent approach to machine learning with impressive performance in many application domains. A well-known use case is for image generation (these models are the main workhorse for tools such as DALL-E and Stable Diffusion) but the same technology has also shown great promise in applications as diverse as probabilistic weather forecasting, biochemistry, and materials discovery. Conceptually, training a generative model is similar to solving a conventional statistical learning problem. Guided by this similarity, the research focus of the current position is to answer the questions: Can we leverage recent advances in generative AI for solving statistical learning problems? Can we leverage state-of-the-art statistical inference methods for improving generative modeling? We will address these questions through novel methodological research resulting in new machine learning models and computational algorithms. We will also work on applied research to demonstrate the usefulness of the new methods, with particular emphasis on the application domains listed above. This is made possible by our active collaborations with applied researchers and domain experts within all of these fields. As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental duties, up to a maximum of 20% of full-time. The work assignments also include actively contributing to the collaborative environment within which the project will be carried out (read more under “Your workplace” below). N.B. When applying for the position we want you to provide a personal letter (first field in the application form). This letter should contain a paragraph where you briefly explain/list the qualifications that you believe are particularly relevant for the research topic described above. This paragraph should start with the words “ Suitability for research topic: ”. Your qualifications You have graduated at Master’s level in machine learning, statistics, computer science, or a related area that is considered relevant for the research topic of the project, or have completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in the subject areas mentioned above. Alternatively, you have gained essentially corresponding knowledge in another way. A successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. The applicant should furthermore have a strong drive towards performing fundamental research; the ability and interest to work collaboratively; and strong communication skills. The applicant should be able to communicate freely in oral and written English. Your workplace Linköping University is one of the leading AI institutions in Sweden. We have strong links to prominent national research initiatives, such as WASP and ELLIIT and you will have access to state-of-the-art computing infrastructure for machine learning, e.g. through BerzeLiUs . The advertised position is part of the Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish society and industry. Read more: https://wasp-sweden.org/ The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/ The position is formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science. At STIMA we conduct research and education in both statistics and machine learning, at the undergraduate, advanced and PhD levels. We regularly publish solid contributions at the best machine learning conferences. STIMA is characterized by a modern view of the statistical subject, where probabilistic models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view of machine learning which clearly integrates the two subject areas within the division. For more information about STIMA, please see https://liu.se/en/organisation/liu/ida/stima The project will be carried out in a collaboration between STIMA (main supervisor: Fredrik Lindsten, senior associate professor in machine learning) and the Division of Systems and Control at Uppsala University (co-supervisor: Jens Sjölund, jens.sjolund@it.uu.se , assistant professor in AI). We will strive for a tight collaboration between the groups, including regular meetings and research visits. As a PhD student in the project, you are expected to actively engage in the teamwork and contribute to this collaboration. The employment When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral studies at each faculty is available at Doctoral studies at Linköping University The employment has a duration of four years’ full-time equivalent. You will initially be employed for a period of one year. The employment will subsequently be renewed for periods of maximum duration two years, depending on your progress through the study plan. The employment may be extended up to a maximum of five years, based on the amount of teaching and departmental duties you have carried out. Further extensions can be granted in special circumstances. Starting date by agreement. Salary and employment benefits The salary of PhD students is determined according to a locally negotiated salary progression. More information about employment benefits at Linköping University is available here. Union representatives Information about union representatives, see Help for applicants . Application procedure Apply for the position by clicking the “Apply” button below. Your application must reach Linköping University no later than May 3, 2024. Applications and documents received after the date above will not be considered. We welcome applicants with different backgrounds, experiences and perspectives - diversity enriches our work and helps us grow. Preserving everybody's equal value, rights and opportunities is a natural part of who we are. Read more about our work with: Equal opportunities . We look forward to receiving your application! Linköping university has framework agreements and wishes to decline direct contacts from staffing- and recruitment companies as well as vendors of job advertisements. Contact persons Fredrik Lindsten Associate professor, Head of division STIMA 46 734 20 16 00 fredrik.lindsten@liu.se Sofie Bondesson HR Administartor sofie.bondesson@liu.se URL to this page https://web103.reachmee.com/ext/I011/853/main?site=7&validator=d7a66c13be778ef950c393a904293789〈=UK&rmpage=job&rmjob=24191&rmlang=UK Apply

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