Six Tenure Track Assistant Professor Positions in Computer Vision and Artificial Intelligence University of Oulu, Faculty of Information Technology and Electrical Engineering

Six tenure track Assistant Professor positions in computer vision and artificial intelligence


The University of Oulu is an international research and innovation university engaged in multidisciplinary basic research and academic education. It is one of the largest universities in Finland with 14 000 students and 3 000 employees. The University encompasses ten fields of study: Architecture, Biochemistry and Molecular Medicine, Humanities, Education, Economics and Business, Science, Medicine and Dentistry, Information Technology and Electrical Engineering, Technology and Mining. The University of Oulu researchers contribute to solving global challenges by combining multidisciplinary approaches, high level research and fruitful collaborations in the following five focus areas: 1. Creating sustainability through materials and systems, 2. Molecular and environmental basis for lifelong health, 3. Digital solutions in sensing and interactions, 4. Earth and near-space system and environmental change, 5. Understanding humans in change. Collaboration across scientific fields is strongly encouraged ad supported within the University. More information http://www.oulu.fi/english/.

 

For strengthening its profile in computer vision and artificial intelligence at international level the University of Oulu announces the following six tenure track Assistant Professor positions for highly talented individuals who hold a doctoral degree and have excellent potential for a successful scientific career:

 

Tenure-track position in learning compact and efficient image and video representations is directed to investigating revolutionary next generation methodologies for image and video representation that are fast to compute, memory efficient, and yet exhibit good discriminability and robustness, bridging the gap between computationally efficient traditional descriptors, e.g., local binary patterns, and very expensive convolutional neural networks. Featuring exponentially increasing amount of images and videos, the emerging phenomenon of big dimensionality (millions of dimensions and above) renders the inadequacies of existing approaches, no matter traditional handcrafted features or recent deep learning based ones. Among the areas of application for compact and efficient representations are wearable/mobile devices with strict low-power constraints, biometric recognition systems, environment embedded perceptual interfaces, and biomedical image and video analysis.

 

Tenure-track position in machine learning for 3D computer vision is directed to developing new machine learning based technology for sensing and analysis of 3D information using image and/or other sensory data. Relevant research topics include but are not limited to pose estimation, multi-view stereo, structure from motion, simultaneous localization and mapping, and 3D object recognition. The aim is to bridge the gap between the conventional geometric approaches and modern learning-based techniques such as deep neural networks. Potential application areas are, for example, augmented reality, robotics, self-driving or autonomous vehicles, computational photography, sensor networks, 3D modeling, and image-based rendering.

 

Tenure-track position in multimodal signal and image analysis for medical and health applications is directed to investigating and designing novel health technologies that apply digital signal or image analysis and machine learning algorithms for improved health and wellbeing. Relevant research topics in signal analysis include cardiovascular system signal analysis, respiratory system signal analysis, central nervous system signal analysis, autonomic nervous system signal analysis, and affective computing. Suitable medical image analysis topics cover a wide range of possible image analysis sub-fields.

 

Tenure-track position in machine learning for human behavior and interaction analysis from multimodal data is directed to emotional artificial intelligence. Research is to investigate and propose novel machine learning methodology, for example for facial expression and body gesture analysis acquired by different types of detectors and sensors, and measuring physiological signals from videos, as well as study the context and work on multi-modal learning and fusion, in order to interpret human behaviors for human-computer/robot interaction with emotional intelligence.

 

Tenure-track position in distributed artificial intelligence and machine learning is directed to distributed environment embedded intelligent system. The objective is to investigate intelligent methods and methodologies for future self-configurable information infrastructures that are expected to consist of very large numbers of wireless sensors and actuators that provide novel services, such as perceptual interfaces though distributed computing and machine learning.

 

Tenure-track position in automated design tool chains for ubiquitous artificial intelligence systems is directed to environment embedded intelligence. The objective is to investigate and propose new tool chains for the automatic generation of intelligent ubiquitous systems, both hardware and executable software, from high-level descriptions, such as dataflow models of machine learning and wireless communications algorithms. The expected applications range from multi-modal signal analysis to environment embedded distributed perceptual human-computer interfaces.


The positions will start as tenure track from September 1, 2018, or later according to mutual agreement with the successful applicant. The researchers appointed to a tenure track position may advance in their career through the tenure track process and be appointed to a Tenure Track Associate Professor position or a Permanent Assistant Professor.

 

The positions are placed at the Center for Machine Vision and Signal Analysis Research Unit (CMVS, http://www.oulu.fi/cmvs), Faculty of Information Technology and Electrical Engineering (ITEE, http://www.oulu.fi/itee). CMVS is renowned world-wide for its scientific breakthroughs in machine vision and signal analysis. Many of its results, including the Local Binary Pattern, face analysis and geometric camera calibration methodologies, are highly cited and have been adopted for different types of problems and applications around the world. The unit is internationally attractive, with one visiting FiDiPro Professor and one Fellow, several visiting scholars and an extensive international collaboration network, enabling a large number of joint publications in leading forums. The main research interests of CMVS are in computer vision and machine learning, affective computing, multimodal image and signal analysis, low-energy computing, and applications in affective human-computer interaction, biometrics, augmented reality, and biomedicine. In physiological signal analysis basic, applied and translational research in biomedical engineering is carried out to tackle key challenges of next generation personalized medicine and wellness solutions.

 

In its field the Research Unit is globally highly ranked with research activities based on international collaborations. The partners of CMVS include three institutes of Chinese Academy of Sciences (Computing Technology, Psychology, and Automation), National University of Singapore, University of Georgia (USA), Imperial College London, Czech Technical University (Prague), University of Maryland (USA), Idiap Research Institute (Switzerland), and EPFL. At the University of Oulu the Research Unit and its leading experts are responsible for undergraduate, graduate, and post-doctoral education in the field. The Assistant Professors will join an international and multidisciplinary research community of researchers from multiple faculties and disciplines that collaborates on new ubiquitous technologies and digital solutions.

 

Job Responsibilities and Required Qualifications: A person at any level of the academic tenure track system is expected to conduct outstanding world-class scientific research, to be competitive in attracting external funding, to publish in leading journals and conferences, to supervise CMVS’s doctoral students, to be an active member of the international scientific community, to create and teach related BSc and MSc level courses, and to exhibit academic leadership.

 

Required qualifications and career advancement at each level of the tenure track: Career advancement on the tenure track is based on performance assessments that measure the candidate’s merits.

 

The position of an Assistant Professor is initially a fixed-term position for two years, with three year continuation period dependent on evaluation. Being granted continuation for the position requires meeting the below-mentioned criteria as well as successful research work as indicated in the University of Oulu Tenure Track guidelines.

 

When appointing a person to the position of an Assistant Professor the applicants are evaluated based on the following criteria:

 

  • publications on an international level: dissemination, quality of the publication forums, references to the publications
  • active role in the research training
  • acquisition of external funding
  • working in more than one research facility during one’s career (in most fields represented at the University of Oulu this signifies working abroad)
  • an active role in the international scientific community
  • acknowledgements and awards.


Salary: The salary of the appointed researcher will be based on the demand level chart for the teaching and research staff of Finnish Universities. In addition to the basic salary of the appropriate tenure track level, supplementary salary will be given for personal achievement and performance, the sum rising to a maximum of 46.3 % of the basic salary level for the post. The salary range is roughly 3500 - 5000 €/month for an assistant professor.

 

Other benefits: Finland is one of the most livable countries, with a high quality of life, safety, excellent education system, and competitive economy. The successful candidate will receive full benefits provided by the University of Oulu to university employees, including free time corresponding to holidays and free occupational health care services. The successful candidate will receive also benefits provided by the Finnish government to residents, for example possibility to obtain access to the national healthcare system, tax benefits for employees with children and high-quality affordable childcare services.

 

Applications: Applications, together with all relevant enclosures, should be submitted electronically by 30.11.2017.

 

The application should be written in English and the following information needs to be included:


1) an application letter with contact information
2) a curriculum vitae following the guidelines of the Finnish Advisory Board on Research Integrity (tutkimuseettinen neuvottelukunta). The guidelines are available at http://www.tenk.fi/en/template-researchers-curriculum-vitae and a template at http://www.tenk.fi/sites/tenk.fi/files/CV_english_270613.pdf.
3) list of publications, ten most important ones marked
4) a brief account of research merits (max 1 page)
5) a brief account of teaching merits or a teaching portfolio (max 2 pages)
6) acquisition of research funds
7) a brief research and action plan (max 3 pages)
8) contact details of 2 - 4 persons available for recommendation.

 

Evaluators: The selection procedure will be carried out by an Appointment Committee according to the University of Oulu Tenure Track guidelines.

 

Contact details: In order to receive the information and announcements concerning the official selection procedures to be followed in order to fill this post, applicants must inform CMVS of their contact details for the whole duration of the selection process: they must specifically provide both their home and work telephone numbers, e-mail addresses, fax numbers and postal addresses.

 

For further information and enquiries about this post, and about the application and selection procedures, please contact:

 

Concerning the application process and practical arrangements:

Planning Officer Elina Rossi

Faculty of Information Technology and Electrical Engineering

P.O.B. 4500, FI-90014 University of Oulu

phone: +358 294 48 4087

email: elina.rossi@oulu.fi

 

Concerning the research related information:

Teacher. Olli Silven

Head, Center for Machine Vision and Signal Analysis Research Unit

Faculty of Information Technology and Electrical Engineering

P.O.B. 4500, FI-90014 University of Oulu

phone: +358 294 48 2788

email: olli.silven@oulu.fi


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