PhD student in adaptive neural networks for deep learning

PhD student in adaptive neural networks for deep learning

Are you interested in devising and implementing novel network structures and learning strategies? Would you like to be part of a research team with skilled and friendly colleagues in an international environment? Are you seeking an employer that offers safe and favorable working conditions?
If so, check out the following PhD position at Uppsala University.

Uppsala University (UU) is the oldest university in Sweden and the Nordic countries. The Department of Information Technology is UU’s third largest department, hosting around 5,000 undergraduate students and 350 employees, including PhD students, academics, and administrative staff. The PhD student will join a dynamic team led by Prof. Orcun Göksel, within the Image Analysis unit of the department’s Vi3 division, working alongside researchers developing numerical and computational methods with a particular focus on deep learning and image analysis.

You can read more here about being employed as a PhD student at Uppsala University.

Project description

Traditional computational approaches require hand-crafting analytical models and/or algorithmic designs. Neural networks have shown great promise in solving complex tasks, where the weights of artificial neural networks are learned using optimization schemes. During such training, the architectures of such networks remain fixed, as predefined initially by an AI specialist. This in practice shifts the hand-crafting step towards selecting and finetuning network designs, which require significant expertise and effort since the choice of network architecture may largely affect final outcomes.

This project will study theoretical, numerical, and implementation aspects of architectureal changes in neural networks. Successful methods will contribute to and help shape the next revolution in deep learning. Succesful solutions will not only streamline and optimize AI model designs, they will also reduce energy, emissions, and effort currently needed to optimize architectures. The resulting models can also be smaller and faster, enabling more powerful applications on embedded devices.

In this project, the successful candidate will conduct basic research and methodological development to design and implement novel computational models and solutions. A solid theoretical background and hands-on experience in deep learning is essential, as well as the same in linear algebra, calculus, and numerical optimization are highly relevant. Developed methods will be tested on toy examples as well as on computer vision applications.

Duties
The doctoral student will primarily devote their time to graduate education. Additional departmental duties, such as teaching assistantship and minor administrative tasks, may comprise up to 20% of the student’s workload.

Requirements
To meet the entry requirements for doctoral studies, you must

• hold a Master’s (second-cycle) degree in computer science, engineering, data sciences, applied mathematics, machine learning, or another related field, or
• have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including an independent project (e.g., a thesis) worth at least 15 credits, or
• have acquired substantially equivalent knowledge in some other way.

We are looking for candidates with:

• A solid computational and analytical understanding and academic background;
• A strong interest in throroughly understanding the nature of existing methods and systems, both in theory and hands-on;
• Proficiency in programming in Python and deep learning frameworks such as PyTorch and TensorFlow;
• Excellent communication skills in oral and written English;
• Creativity, thoroughness, and a structured approach to problem-solving.

Consideration will also be given to good collaborative skills, drive, and independence, and how the applicant’s experience and skills complement and strengthen ongoing research within the department, and how they stand to contribute to its future development.

Additional qualifications
Experience with calculus, linear algebra, optimization, probability theory, and numerical methods is desirable. Knowledge of Matlab is an advantage.

Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines. For special entry requirements, read more here.

The application must include:

1. A CV;
2. Degrees and transcripts with grades (with course name translations in English or Swedish), with the rank/percentile in the graduating class or within a large comparison group indicated in the CV;
3. A short cover letter (at most one page) containing:
   • earliest possible start date (with reasons if applicable);
   • a brief bullet-point list of three major academic or scientific achievements;
   • (optional) a bullet-point list for additional explanations that the applicant may wish to clarify (e.g., limited experience in a qualification aspect, gaps between degrees, or academic delays) or other remarks, if any.
4. A reading sample, e.g., Master’s thesis (or its draft) or another self-produced scientific text. For contributions to joint publications, include a list in the CV with links to papers online, and add the publication with the applicant’s largest contribution as a second reading sample;
5. Contact details (names, emails, and telephone numbers) of minimum two references, also specifying the context, duration, and nature of the relationship with the candidate. Reference letters may be provided as supporting document.

About the employment
The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date Spring 2025 or as agreed. Placement: Uppsala

For further information about the position, please contact: Orcun Göksel, Professor, Department of Information Technology, orcun.goksel@it.uu.se.

Please submit your application by 21 January 2025, UFV-PA 2024/4263.

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