We are seeking new members to our computer vision and machine learning research teams in Helsinki, Tampere, and Oulu in Finland. We offer several positions ranging from 1 to 4 years on levels of:
- Masters thesis worker
- PhD candidate
- Postdoctoral researcher
- Research engineer
- Research residency for a software professional
The positions will offer excellent opportunities to work in a team of professionals responsible for developing cutting edge computer vision and machine learning technology, and allows you to learn many aspects from fundamental research problems to concrete applications.
The research residency enables software professionals to join an academic research team for a fixed time period and to extend their knowledge in the state-of-the-art computer vision and machine learning methods. The length of the residency period is agreed individually and varies from a few months to one year.
We welcome applications with any research focus related to computer vision and machine learning. In addition, we are especially seeking candidates with the following research focus:
- Visual localization
- Visual-inertial odometry
- Simultaneous localization and mapping (SLAM)
- 3D scene reconstruction
- Machine learning based image reconstruction and manipulation
- Sensor fusion and statistical machine learning
- Augmented reality for games and mobile applications (Android and/or iOS)
Ideal candidate has a strong programming and mathematics background. Experience in (or strong will to learn) programming with Python or C++/Java are considered as advantages. Previous experience on Android/iOS programming is also beneficial, particularly for the research engineer positions.
Team and research
The Principal Investigators of the projects are Professor Janne Heikkilä (University of Oulu), Assistant Professor Juho Kannala (Aalto University), Assistant Professor Esa Rahtu (Tampere University), and Assistant Professor Arno Solin (Aalto University). We work broadly in the fields of computer vision and machine learning. We are pursuing research problems in geometric computer vision (including topics such as visual SLAM, visual-inertial odometry, and 3D scene reconstruction), in semantic computer vision (including topics such as image-based localization, object detection and recognition, and deep learning), and statistical machine learning (Gaussian processes). More information of our research is available on our web pages linked below. (You can follow the links by clicking the images.)
Some of our previous works are illustrated in the following. You can find more information from the references below.
We have developed a visual-inertial odometry method based on an information fusion framework employing low-cost IMU sensors and the monocular camera in a standard smartphone. Our approach utilizes strong coupling between inertial and visual data sources which leads to robustness against occlusion and feature-poor environments. The video demonstrates the performance of our approach based on standard smartphone sensors.
Image based localisation and SLAM
We have investigated machine learning based approaches for visual localization and SLAM systems. For instance, we have developed a CNN-based scene coordinate regression method for image-based localization. The new model can be trained without careful initialization, and the system achieves accurate results. Another example is a method for scalable and fully 3D magnetic field SLAM using local anomalies in the magnetic field as a source of position information. The video illustrates the magnetic field SLAM method in practice.
Multi-view stereo by temporal nonparametric fusion
In our recent work, we proposed a novel idea for depth estimation from unstructured multi-view image-pose pairs, where the model has capability to leverage information from previous latent-space encodings of the scene. The following video demonstrates a real-time implementation of the method on a mobile device.
Adversarial learning of image representations
We have studied methods for learning image representations using generative adversarial networks (GANs). Our works in this area include image-to-image translation models, which are able to learn from both paired and unpaired training examples. Furthermore, one of our most recent works utilises generative adversarial networks for implementing a light-weight generic face animator that is able to control the pose and expressions of a given face image. The video shows multiple face manipulation examples obtained using our method.
Aalto University, Tampere University, and the University of Oulu are the leading universities in engineering and technology in Finland.
The Computer Science Department at Aalto provides world-class research and education in modern computer science to foster future science, engineering and society. The work combines fundamental research with innovative applications. The department is routinely ranked among the top 10 CS departments in Europe and in the top 100 globally.
The Department of Signal Processing at Tampere University has 170 members of which 30-40% are of foreign origin. The department has held the prestiguous status of a Center of Excellence in Research (CoE) elected by the Finnish Academy of Sciences. Core areas of research include image, video and audio signal processing and analysis as well as machine learning related topics.
The Center for Machine Vision Research (known as MVG until 2011) at the University of Oulu was established already in the beginning of 1980s. One of the research highlights of the group is the Local Binary Pattern methodology and its various applications, for example, in face image analysis. In addition, the group has strong expertise in geometric image and video analysis, texture analysis, and industrial applications of machine vision.
Working and studying in Finland
Finland has been assessed to be among the best countries in the world with respect to many quality of life indicators, including being the overall #1 country in human wellbeing. Finland is among the leading countries in ICT & Digitalization, from 5G networks to financial technology, artificial intelligence and AR/VR innovations to IoT, digital education and health solutions. Finland’s booming gaming industry is led by companies like Supercell (Clash of Clans) and Rovio (Angry Birds). More information about studying and working in Finland can be obtained from the links.
The starting salary of a PhD student is ca. 2400 EUR per month and it will increase during the studies depending on the progress (up to 3100 EUR per month). The salary for a postdoctoral researcher starts typically from 3 500 EUR per month, and increases based on experience. The salary of a research engineer and research resident varies between 3000 - 5000 EUR depending on the experience.
In addition to the salary, the contract includes occupational healthcare benefits, and Finland has a comprehensive social security system. The positions are located at Aalto University, Tampere University, and the University of Oulu.
How to apply
The first stage of the application process is very light. There is no need for motivation letters or research statements. You only need to email the following documents to the address finland.cvml(at)gmail.com:
- Curriculum vitae
School and university score records
- Indicate also the position type using the following tag(s) in the email subject:
[msc], [phd], [postdoc], [engineer], or [residency].
The deadline for the applications is June 26th, 2019. The review of applications will be done on a rolling basis, so early submissions are strongly encouraged. The starting date of the positions will be negotiated individually.
For a complete list of our works, see our homepages and arXiv.
Cortes S, Solin A, Rahtu E, Kannala J (2018), ADVIO: An authentic dataset for visual-inertial odometry, European Conference on Computer Vision (ECCV), pdf, project page
Hou Y, Kannala J, Solin A (2019), Multi-View Stereo by Temporal Nonparametric Fusion, arXiv preprint pdf, project page
Kok M, Solin A (2018), Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps, International Conference on Information Fusion (FUSION), Best paper award, pdf, YouTube
Li X, Ylioinas J, Verbeek J, Kannala J (2018), Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization, Geometry Meets Deep Learning ECCV 2018 Workshop, pdf
Melekhov I, Tiulpin A, Sattler T, Pollefeys M, Rahtu E, Kannala J (2019), DGC-Net: Dense geometric correspondence network, IEEE Winter Conference on Applications of Computer Vision (WACV), Best paper honorable mention, pdf, project page
Mustaniemi J, Kannala J, Särkkä S, Matas J, Heikkilä J (2019), Gyroscope-Aided Motion Deblurring with Deep Networks, IEEE Winter Conference on Applications of Computer Vision (WACV), pdf
Otani M, Nakashima Y, Rahtu E, Heikkilä J (2019), Rethinking the Evaluation of Video Summaries, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pdf, project page
Solin A, Cortes S, Rahtu E, Kannala J (2018), PIVO: Probabilistic Inertial-Visual Odometry for occlusion-robust navigation, IEEE Winter Conference on Applications of Computer Vision (WACV), pdf, project page
Solin A, Cortes S, Rahtu E, Kannala J (2018), Inertial Odometry on Handheld Smartphones, International Conference on Information Fusion (FUSION), pdf, project page
Tripathy S, Kannala J, Rahtu E (2019), ICface: Interpretable and Controllable Face Reenactment Using GANs, arXiv preprint pdf, project page
Tripathy S, Kannala J, Rahtu E (2018), Learning image-to-image translation using paired and unpaired training samples, Asian Conference on Computer Vision (ACCV), pdf, project page