Doctoral Researcher (Signal Processing and Machine Learning (DSII))

Tampere University and Tampere University of Applied Sciences create a unique environment for multidisciplinary, inspirational and high-impact research and education. Our universities community has its competitive edges in technology, health and society. www.tuni.fi/en

The Doctoral School of Industry Innovations (DSII) at Tampere University (TAU) explores research questions that are directly relevant to industry and business. Tampere University hires a Doctoral Researcher to complete a four-year dissertation project under the joint supervision of two university professors and two company representatives. The position is placed in the 3D Media Group, which is part of the Computing Sciences Unit within the Faculty of Information Technology and Communication Sciences (ITC). 

DSII offers Doctoral Researchers a unique opportunity to undertake dissertation research, gain a thorough understanding of product development, tackle real-world business challenges and expand their professional networks.

The partner company for this position is Microsoft, through its Surface Imaging Team in Tampere, which researches and develops new technologies in the fields of imaging, AI, mobile devices, and electronics. 

Job description

The purpose of the job is to create new knowledge for academia and industry in the fields of computational imaging, optics and machine learning.  The research project aims at jointly optimizing the optics and inverse imaging algorithms for compact camera designs employing machine learning to achieve image quality for a certain task.   

The research goals are specified as follows:

1. Develop models of refractive optics applicable to compact optics used in portable devices that ensure realistic and manufacturable compact optics design
2. Develop ML based post-processing imaging methods to couple with the optical models
3. Develop end-to-end imaging system optimization methods to co-design compact optics and efficient ML algorithms
4. Validate the end-to-end methodology on selected case(s) of image restoration and enhancement 

As a Doctoral Researcher you are expected to perform overviews of state of the art, to study the principles of physical image formation and its modelling in terms of optics, sensing and image processing, to investigate methods for optical/imaging co-design employing modern machine learning an develop the corresponding algorithms, to generate experimental results, analyse and validate them on use case(s) through comparison with the state of the art. 

The results of your research will be disseminated in scientific peer-reviewed journals, international conferences, doctoral network events and events with industry. The tasks of doctoral researchers also include teaching duties that amount to approximately 5% of their annual working hours, unless there are justified reasons to the contrary.

At the university, the work will be supervised by Prof. Atanas Gotchev and Prof. Humeyra Caglayan. You will also be supported by practitioners in the steering group, including company representatives. 

By participating in DSII events, excursions, meetings and training sessions with your fellow doctoral students, you will both get and provide peer support and make your own contribution to the development of DSII.

Requirements

You need to have an applicable master’s degree in signal processing, electronics, physics, optics and photonics, information technology, computer science, or a related discipline in science or engineering. 

The position requires:  

• Strong background at MSc level in: 
  • Mathematics: linear algebra, numerical methods, probability and statistics
  • Signal and image processing: sampling and reconstruction of signals, Fourier transform, image formation and image processing
  • Optics: basics of geometrical and wave optics
• Good command of both written and spoken English

Experience in some of the following fields is considered as a plus:

• Vector-space formalism
• Constrained and unconstrained optimization
• Optical design with Zemax or similar software
• Machine learning
  • Neural networks: DNN, CNN, RNN, Transformers
  • Learning: Loss functions, differentiation and. Back propagation, stochastic gradient descent, regularization
  • Deep Generative models: plug-and-play models, variational autoencoders, GANs 

You also need to have an enquiring mind, enjoy problem-solving and be able to think independently while also being able to work in a team. 

Please note that before commencing in the position, the selected candidate must apply for a study right within the doctoral programme at Tampere University, unless they already have one. Please visit the admissions webpage for more information on eligibility requirements.

Tampere University is a unique, multidisciplinary and boldly forward-looking, evolving community. Our core values are openness, critical thinking, diversity, learner-centredness, courage, erudition and responsibility. We hope that you can embrace these values and promote them in your work.

We offer

The position will be filled for a fixed-term period of four (4) years, commencing in 01.10.2024 or as soon after that as possible. A trial period of six (6) months applies to all our new employees. Remote work opportunities are agreed separately when needed.

The salary will be based on both the job requirements and the employee's personal performance in accordance with the Finnish University Salary System. According to the criteria applied to teaching and research staff, the position of a doctoral researcher is placed on level 2-4 of the job requirements scale. A typical starting salary for a Doctoral Researcher is approximately 2700 EUR per month. The salary increases based on experience and the progress of doctoral studies.  

We invite you to participate a vibrant, active and truly international research community. The research will be conducted at the collaborative environment created by the Microsoft Surface Imaging Team and the 3D Media Group at the Signal Processing Research Centre, led by Prof. Gotchev and and the Metaplasmonics Group, led by Prof. Caglayan.  Regular interaction between the three research teams, including project meetings and social gatherings, will foster a fruitful and productive research environment.

The University has a strong commitment to the highest level of scientific research and the provision of high-quality doctoral education. The faculty as well as the Doctoral School of Industry Innovations support the doctoral students by offering training and peer support. Doctoral students in DSII develop vital skills, enabling them to conduct agile, customer-oriented research, development and business.The University offers a wide range of staff benefits, such as occupational health care, flexible working hours, excellent sports facilities on campus, and several on-campus restaurants and cafés with staff discounts. Please read more about working at Tampere University. 

Tampere is one of the major academic hubs in the Nordic countries and offers a dynamic living environment. Tampere region is one of Finland´s three most rapidly growing urban areas and home to a vibrant knowledge-intensive entrepreneurial community. The city is an industrial powerhouse that enjoys a rich cultural scene and a reputation as a centre of Finland’s information society. For more information on Tampere, please visit here.

How to apply

Please submit your application through our online recruitment system. The closing date for applications is 27.09.2024 (23:59 EEST / 21.59 UTC). Please write your application and all the accompanying documentation in English and attach them in PDF format.

Please attach only the following documents to your application: 

• A letter of motivation and description of your research interests (max. 1 A4 page)
• Curriculum vitae according to the TENK template  (including the contact details of two referees).
• List of publications
• Degree certificates
• Transcript of records (master's degree studies).

For further information, please contact:

Prof. Atanas Gotchev (atanas.gotchev@tuni.fi)

Prof. Humeyra Caglayan (humeyra.caglayan@tuni.fi)