Doctoral student in Computer Science with focus on methods and tools for Neuromorphic Computing

Description of the workplace

The position is in the Embedded Systems Design (ESD) group of the Parallel Systems (PS) division, part of the Computer Science (CS) department. The group drives research related to design methods, languages and tools for resource constrained systems, where optimizing energy, memory, timing, and cost are of main interest. The group members have expertise in a wide range of domains covering both hardware and software, including compilers, operating systems and algorithms. The ESD group also has active connections to local industry, such as Arm and Axis, other Faculty of Engineering (LTH) departments and other universities both abroad, such as the Danish Technical University, and in Sweden, such as Linköping University and Blekinge Technical University. The PS division, with 21 senior and student members, is one of the larger in the department; it includes two other groups with expertise in computer graphics and theoretical computer science, respectively. The CS department has a very social group of students that can help you settle in. There are also opportunities to socialise and network with other PhD students at LTH.

More about PS

Being a doctoral student 

As a doctoral student, you are both admitted as a student and employed at Lund University. 

As a doctoral student, you will be trained in a scientific approach. In short, you will be trained to think critically and analytically, to solve problems independently using the right methods, and to develop an awareness of research ethics. In addition, you will have the opportunity to work on projects, to develop your leadership and pedagogical (teaching) skills. Throughout your studies, you will be guided by supervisors. Doctoral studies end with a thesis and a doctoral degree.

More about being a doctoral student at LTH on lth.se. 

Subject and project description

Neuromorphic computing is a field gaining interest due to its promise of bringing energy efficiency to neural network inference and training. The work planned in this project will focus on efficient compilation and runtime environments for Neuromorphic hardware. Some of the goals targeted are reducing energy consumption, and hardware cost, increasing performance and reliability. You will receive plenty of guidance but also have the freedom to take the project in the direction you feel most comfortable with, a direction that allows you to take charge of your own research within the larger project.

The announced position is part of an ELLIIT funded project, called “ScEENeC – Scalable, Energy-Efficient Neuromorphic Computing” which is a cooperation between LTH and two other universities, namely Blekinge Technical University (BTH) and Linköping University (LiU). As such, the work is intended to be carried out in collaboration with two other PhD students (one from BTH and one from LiU) towards advancing the goals of ScEENeC. This is a continuation of a successful earlier ELLIIT funded project, “GPAI – General Purpose AI Computing”, hosting three other doctoral students soon to graduate. 

More about ELIIT
More about GPAI
More about ScEENeC

Work duties

You will primarily devote yourself to your doctoral programme, which includes participation in research projects as well as third cycle (PhD student level) courses, seminars and conferences. 

The work duties include:

• You will carry out research on code compilation methods for Neuromorphic platforms; on novel intermediate representations and optimizations for low energy, low latency or high throughput; on runtime environments and techniques for the same goals.
• You will be extending existing compiler frameworks, such as MLIR, with tools, optimization algorithms, analysis methods, and dialects specific for Neuromorphic backends.
• You may be required to work with and improve runtime environments for Neuromorphic systems, subject to available hardware.
• You will be evaluating realistic applications on the tools developed within the project, for the available hardware. The type of applications primarily include Spiking Neural Networks (SNN), both for inference and training.
• You will be required to present your research results during the regular project meetings as well as wider fora, possibly in different locations in Sweden and abroad (about 1—2 times per year).
• You will be required to collaborate with the other members of the ScEENeC project, as the research dictates. This may involve shorter visits (1 week/visit/year) at the partner universities.
• You are encouraged to collaborate with other academic and industry actors, especially if this promotes ScEENeC or your research within the project.
• The duties also include participation in teaching and other departmental work (however, a maximum of 20% of working hours).

Qualifications 

To be eligible for admission and employment as a doctoral student, you must fulfil the requirements below.

Admission requirements 

A person meets the general admission requirements for third-cycle courses and study programmes if the applicant: 

• has been awarded a second-cycle qualification, or 
• has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or 
• has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

A person meets the specific admission requirements for third cycle studies in Computer Science if the applicant has: 

• at least 60 second-cycle credits at an advanced level with relevance for the subject topic, or
• a MSc in Engineering in Computer Science and Engineering, Electrical Engineering, Information and Communication Technology, Engineering Physics, or Engineering Mathematics. 

Additional requirements

In order to complete the doctoral programme in question, the following are also required: 

• good knowledge of the following areas (e.g. passed relevant courses or acquired this knowledge practically in other acredited way):
  • compiler construction
  • neural networks principles
• experience with both (e.g. as a graduation project, individual project, projects in a industrial or academic work environment) 
  • developing compilers or contributing to compiler toolchains
  • building, training and using neural networks
• ability to take initiative, structure work, plan and follow through (e.g. started and developed own projects, groups, events, or other initiatives)
• good ability to work independently and to formulate and tackle research problems 
• good written and oral communication skills
• good ability to cooperate
• very good knowledge of English, spoken and written 

Other qualifications 

For the doctoral programme in question, experience in any of the following is not required, but considered beneficial for your application:

• good knowledge of advanced algorithms and data structures (e.g. aquired via a university programme or demonstrable individual project experience)
• knowledge of low level programming, processor architecture and hardware in general
• basic knowledge of systems programming, operating systems and real-time systems
• very good C and C++ skills and programming experience
• basic knowledge of other mainstream programming or scripting languages
• experience with MLIR compiler construction framework, including dialect development and optimization passes
• experience with other large compiler frameworks, both at intermediate representation level and backend, for code generation 
• experience with software development practices in one of the areas of interest
• experience with scientific writing (e.g. as author or co-author of scientific publications, technical reports or similar relevant documents) 

We offer

Lund University is a public authority which means that employees get particular benefits, generous annual leave and an advantageous occupational pension scheme.

As a PhD student, you are also an employee benefiting from these advantages. Your funding also includes a generous travel budget to attend international conferences and possible research visits to other universities. At Lund University we also offer the opportunity to take PhD level courses focusing on cutting edge research. You and your supervisors will have the flexibility to steer your education in the way that is most interesting and beneficial to you.

More about working at Lund University on lu.se.

About the employment 

The employment is a fixed-term employment at full time, starting as soon as possible by agreement, but no later than December 15, 2026. Third cycle studies at LTH consist of full-time studies for 4 years. In the case of teaching and other departmental duties, the employment is extended accordingly. Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

More about terms of employment for doctoral students on Lund University’s Staffpages. 

How to apply

Applications shall be written in English and include: 

• CV and a cover letter stating the reasons why you are interested in the doctoral programme/employment and in what way the research project corresponds to your interests and educational background. 
• Copies of issued study certificates and/or awarded degree certificates. These must confirm that you meet the general and specific admission requirements for the doctoral programme and show that you have the subject knowledge required for the doctoral programme project. 
• Other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.)

We welcome your application.