Doctoral student in Electrical Engineering oriented towards Algorithm-hardware co-design for energy efficient AI - Hiring in process/Finished, not possible to apply

Subject description
One of the important steps for artificial intelligence (AI) applications is that the AI devices are able to understand the environment and react appropriately in real time. This is a very challenging task given the large amount of sensor data to be processed and the highly computational complex algorithms adopted to deliver high accuracy. Energy efficient AI, from the design of energy efficient AI algorithms to the implementation of energy efficient AI processors/accelerators, is becoming an important research topic, especially for energy source limited applications such as smart wearable devices and battery-powered autonomous vehicles.

The research area of the doctoral student deals with digital hardware implementation of processing algorithms, including machine learning, for AI applications related to visual. More specifically, the subject includes methodologies for the development of AI processing algorithms, digital processor/accelerator architectures, and the corresponding integrated circuits realization.

Work duties
The research will involve activities at both system/algorithm and hardware/circuit levels. The main work duty is to perform research on mapping learning and signal processing algorithms into digital circuits, targeting at high performance embedded systems with very high energy efficiency. The research tasks also include the development of hardware-friendly AI algorithms, thus tightly coupling the design of algorithms with the implementation of hardware architectures. The system-level features and algorithm profiling will guide the hardware architecture design, while hardware characteristics will also be explored for algorithm development and optimization.

With real-time processing capability and low power consumption as the design target, the outcome of this research will make AI available and deployable to real-world embedded applications. More specifically, we are aiming for visual-based (assisted) learning for smart wearable devices and autonomous aerial vehicle, including tasks like object detection, feature tracking, localization, mapping, etc.

The student will work, together with senior researchers and other PhD students, in a joint research effort between the Department of Electrical and Information Technology and Centre for Mathematical Sciences.

The main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects and third cycle courses. The work duties also include teaching and other departmental duties (no more than 20%).

Admission requirements
A person meets the general admission requirements for third-cycle courses and study programmes if he or she:

• 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 Electrical Engineering if he or she has:

• at least 60 second-cycle credits in subjects of relevance to electrical engineering, or
• a MSc degree in biomedical engineering, computer science, electrical engineering, engineering mathematics, nanoengineering, engineering physics, information and communications engineering, or related areas.

Additional requirements:

• Very good oral and written proficiency in English.
• Deep knowledge and experience in design and implementation of digital integrated circuits and processor architecture.
• Good knowledge and understanding of artificial intelligence systems, machine learning, and optimization.
• Hands-on experience with real AI and learning systems (simulation, implementation, etc.)
• Function well in a team.

Assessment criteria
Selection for third-cycle studies is based on the student’s potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Special attention is paid to the following:

1. Knowledge and skills relevant to the thesis project and the subject of study.
2. An assessment of ability to work independently and to formulate and tackle research problems.
3. Written and oral communication skills
4. Other experience relevant to the third-cycle studies, e.g. professional experience.

Other assessment criteria:

• Courses, master's thesis, and experience in design of digital integrated circuits.
• Experience in using hardware description language and CAD-systems for digital integrated circuit design.
• Experience and strong interest in design and implementation of signal processing algorithms for AI applications (e.g, computer vision and robotics)
• Knowledge of computer vision will be a beneficial asset.

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant, through his or her experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme.

Conditions
Only those admitted to third cycle studies may be appointed to a doctoral studentship. Third cycle studies at LTH consist of full-time studies for 4 years. A doctoral studentship is a fixed-term employment of a maximum of 5 years (including 20% departmental duties). Doctoral studentships are regulated in the Higher Education Ordinance (1998:80).

Instructions on how to apply
Applications shall be written in English and include a cover letter stating the reasons why you are interested in the position and in what way the research project corresponds to your interests and educational background. The application must also contain a CV, degree certificate or equivalent, and other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.).