100 open positions for doctoral researchers in AI at Finnish Universities
Jörg Tiedemann
Call for Doctoral Students in Artificial Intelligence
The Finnish Doctoral Program Network in Artificial Intelligence is looking for 100 new PhD students to work in fundamental AI and machine learning research and in five application areas. Come do a PhD tackling challenging research questions in a network that fosters industry and multidisciplinary collaboration!
- Call website: https://fcai.fi/doctoral-program
- Deadline: April 2, 2024
JOB DETAILS
The positions are based at one of the ten universities that are part of the Finnish Doctoral Program Network in Artificial Intelligence. The recruiting university will be the same as that of the primary supervisor. The matching of the candidates with supervisors will be done during the review process and the candidates will have a chance to prioritise the supervising professor they want to work with (see details in FAQ).
All positions are fully-funded. PhD student contracts will be made for three years. The terms of employment and the salaries are based on the General Collective Agreement for Universities. The contract includes occupational healthcare.
We are looking for 100 new PhD students in two calls (in spring and fall 2024). The accepted candidates of the spring call are expected to start in August 2024, and the applicants from the fall call in January 2025.
HOW TO APPLY
We are looking for 100 new PhD students to join the Finnish Doctoral Program Network in Artificial Intelligence in two calls: the first one is open March 11–April 2, 2024 and the second will open in fall 2024.
Candidates will apply to all universities and application areas with the same joint application. In the application form, you are able to indicate which specific research areas and supervisors you are interested in. Note: Candidates who apply to supervisors based at the University of Helsinki, will have to submit a parallel application to the university’s own recruitment system. Please note that the application needs to be submitted to both of the recruitment systems to ensure a proper review. See further details.
The deadline for applications in the ongoing call is April 2, 2024. Please submit your application in our online recruitment system with the required attachments (detailed below).
Required attachments:
- Motivation letter (1–2 pages). Please specify the research area(s) and preferably the supervisors with whom you want to work.
- CV
- List of publications (if relevant; please do not attach full copies of publications)
- A transcript of master’s/bachelor’s studies and the degree certificate of your latest degree. If you don’t have a Master's degree, a plan of completion must be submitted.
In the application form, you are also asked to provide contact details of two senior academics who can provide references.
All materials should be submitted in English in a PDF format. Note: You can upload max. five files to the recruitment system, each max. 5MB.
RESEARCH AREAS
FUNDAMENTAL AI
Fundamental AI methods are the core of the FCAI research activities and the cornerstone in all application areas. Fundamental AI encompasses probabilistic AI for verifiable and uncertainty-aware model building, simulation-based inference for efficient and interpretable reasoning capabilities, data-efficient deep learning, privacy-preserving and secure AI, interactive AI for collaborative AI tools, autonomous AI, statistics, and decision-making. Widely applicable goals of the fundamental AI are AI-assisted decision-making, design and modeling.
Keywords: Artificial Intelligence, Causal Inference, Collaborative AI and human modeling, Machine Learning, Statistics
AI IN LANGUAGE AND SPEECH TECHNOLOGY
The area covers all aspects of natural language processing (NLP), a field of research dealing with computational analysis and generation of human language. NLP is a broad field which spans from highly technical research on machine learning techniques for written and spoken language data, through the myriad of individual tasks such as machine translation and information retrieval, to digital linguistics. The field is reliant on very large datasets and high performance computing, offering exciting software engineering and algorithmic challenges. Finland has a long tradition of top-notch NLP research, especially in the multilingual setting and, recently, large language model development.
Keywords: Foundation models, Human language technology, Natural Language Processing, NLP, Large language models, Speech recognition, Speech generation, Machine translation, Crosslingual models
AI IN COMMUNICATIONS AND SIGNAL PROCESSING
The area covers a wide range of advanced methods in communications and distributed intelligence technologies, statistical methods in signal processing, and analysis of images, video, speech, audio and array signals.
The methodologies can be applied in various layers of communications systems from applications to the radio connectivity with distributed intelligence that is an integral part of next generation communication and computing systems targeting to solve issues related to ultra densification of infrastructure, devices and people, and to guarantee secure, low latency and reliable use of ICT resources using advanced AI methods.
This research area also includes acquiring, processing, analyzing and understanding digital images, video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, 3D point clouds from LiDaR sensors, or medical scanning devices, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions, using models constructed with the aid of geometry, physics, statistics, and learning theory.
Keywords: Array signal processing, Computer vision, Edge intelligence, Perception, Sensors, Wireless communications
AI IN HEALTH
The health and wellbeing field holds high potential to profit from advances in AI. Applications range from personalized care and precision medicine to preventive care and to process optimization. Increasing availability of large amounts of multi-source data combined with novel AI paradigms give huge opportunities. Challenges are how to extract valid actionable knowledge from all that data, how to develop AI-based solutions that are trustworthy, fit into healthcare processes, and that have an actual impact.
Keywords: Biomedical Image and Signal analysis; Multi-modal Health Data Analysis; Predictive, Preventive, Personalized, Participatory Healthcare, Trustworthy AI, Healthcare Processes
AI IN ENGINEERING
Industries are currently employing AI methods in numerous research and development tasks. Examples include product design, predictive maintenance, and combining physical models with data-based methods. There is a great potential also in replacing laboratory development and experiments with virtual laboratory-type approaches. Research topics include:
- AI methods in industrial research and development, including:
- AI for product design and optimization, combining physic-based and data-driven models.
- AI for improving industrial operations: cyber security, anomaly detection in industrial time series and predictive maintenance.
- Methods supporting AI in industrial deployments, including on-device learning and federated learning on edge devices.
- Virtual laboratories for experimentation and cost-effective product design and validation.
- AI methods for autonomous functions in land, sea, air and space vehicles and machines. These range from pilot assistance, collision avoidance and navigation systems to full-mission autopilots.
Keywords: Autonomous systems, Energy systems, Machine automation, Manufacturing, Materials, Mechanical engineering, Robotics
AI IN SOCIETY AND BUSINESS
The area examines the societal, ethical, and economic dimensions of AI, including trustworthy and societally acceptable AI as well as the consequences of the uses of AI. It brings together AI research with social sciences and humanities to gain in-depth understanding of AI’s role in organizations, society, business, and the economy. It includes uses of AI in education and education about AI. The area fosters interdisciplinarity to reinforce cross-cutting themes such as sustainability, ethics, equity, trust, and social responsibility.
Keywords: AI in business operations, AI in society, AI and Education, AI Ethics
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Jörg Tiedemann
Language Technology https://blogs.helsinki.fi/language-technology/
University of Helsinki