We are seeking new PhD students and postdocs to our machine learning, artificial intelligence, robot and control, and signal processing research teams in Tampere in Finland.
The positions will offer excellent opportunities to work in a team of professionals responsible for developing cutting edge technologies, and allows you to learn many aspects from fundamental research problems to concrete applications.
We welcome applications with any research focus related to machine learning, artificial intelligence, robot and control, and signal processing. 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.
The doctoral candidates will be supervised by PIs from the departments of Computing Sciences and Automation Technology and Mechanical Engineering. We work broadly in the fields of computer vision, machine learning, robotics, and control. We are pursuing research problems in many fields of artificial intelligence and robotics. More information of our research is available on our web pages linked below. (You can follow the links by clicking the images.)
As the Advanced Learning, Control, and AutomatioN (ALCAN) Research Group, we work on the safety and reliability of complex autonomous systems that interact directly with people. We invite applications for a fully funded Doctoral Researcher position to conduct research in our recently awarded 4-years project, Human Intention Aware Control Strategies for Autonomous Driving (HUMINAC). The project focuses on developing novel, human intention–aware control strategies at the intersection of machine learning, reinforcement learning, and optimal control. Our work aims to make a significant impact not only on autonomous driving but also across robotics and autonomous systems more broadly.
Note: This position is located at ams OSRAM Research Center, Jena, Germany
Color constancy makes sure that colors look the same in different illuminations. In digital cameras, color constancy - or Auto White Balance (AWB) - is done by by estimating the color of illuminant and by normalizing the sensor values like they would have been captured under a white light source. However, the estimation often fails and leads to poor results. Very recently, we have developed a method based on spectral sensing which is more accurate representation of color than RGB. Understanding spectral color constancy is highly important for camera manufacturers and for the manufacturers of spectral sensors. Our approach is described in this paper or alternatively you can watch the video at the BMVC web site.
The research groups of Financial Computing and Data Analytics and Computational Intelligence, led by Professors Juho Kanniainen and Alexandros Iosifidis, have been pioneers in developing low-latency machine learning models for Limit Order Book (LOB) driven financial markets. Together with their international collaborators, they have published in top-tier AI journals and rank among the most cited researchers in this field. Their TABL model, which has gained significant international attention and recognition, along with its derivatives, stands among the most influential and highest-performing models for LOB prediction. For more information see our paper.
We are inviting applications for a doctoral researcher (PhD student) position in Signal Processing and Machine Learning. The research topic – to be tailored to the interests of the candidate – will broadly lie in the intersection of statistical signal processing, optimization and machine learning, with an emphasis on theory and algorithms. Key focus applications include array signal processing for sensing (e.g., radar, RF imaging, sonar) and wireless communications, where multisensor (MIMO) systems are, and will continue to be, a core technology. For a flavor of the type of research to be expected see the recent papers in Google Scholar.
The research groups of Cognitive Robotics and Autonomous Mobile Machines, led by Professors Roel Pieters and Reza Ghabcheloo develop AI-based solutions for robots and mobile machinery. Research topics for the Cognitive Robotics research group relates to robot manipulation and human-robot collaboration with AI-based methods: a) learning for robot manipulation by Foundation models (VLM, LLM) and/or, b) perception, action and learning for robot manipulation and human-robot interaction. For recent projects and papers see: link
Research topics for the Autonomous Mobile Machines group relate to robot learning and optimization for a) planning and control for autonomous navigation, and manipulation in non-road applications; b) robust perception and situational awareness in challenging outdoor environments. For recent projects and papers see: link
The generation of visually relevant, high-quality sounds is a longstanding challenge of deep learning. Solving this challenge would allow sound designers to spend less time searching large foley databases for the sound that is relevant to a specific video scene. We approach the visually guided sound generation by shrinking a training dataset of audio spectrograms to a set of representative vectors aka. a codebook. Similar to word tokens in language modeling, these codebook vectors can be used by a transformer to sample a representation that can be easily decoded into a spectrogram and subsequently to audio stream. The video explains our approach presented in this paper
Despite its many successes, the current RL paradigm is too simplistic to develop reliable algorithms for training agents directly in real-world settings at their full scope and complexity. In particular, humans (especially children) are driven by curiosity to explore the world (first figure). Can RL agents exhibit the same behavior? For example, agents could be intrinsically driven to interact with keys and doors in MiniGrids (second figure), collect resources in Crafter (third figure), or climb ladders in Montezuma's Revenge (fourth figure). Just as humans can easily identify interesting elements common to these video games, an agent should be able to transfer concepts of interestingness and curiosity between environments and plan its actions to maximize the discovery of unexplored areas. To achieve such RL agents, this project will focus on foundation models, intrinsic motivation, and exploration in RL.
Tampere University is the leading university in engineering and technology in Finland.
The 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 institute is also part of the European Laboratory for Learning and Intelligent Systems (ELLIS) that provides a strong network of the best European researchers.
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 3500 EUR per month, and increases based on 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 and Tampere University.
The applications are submitted to the application portals of the corresponding universities/institutes. We highly recommend you to apply through all portals, even if you have a clear preference on which position you prefer, this can be decided later in any case.
The links to the application portals:
If you have any questions regarding the positions or the applications, please contact the listed PIs.
