Postdoctoral Fellow (# of pos: 78)

Aalto University and the University of Helsinki have formed a

strategic partnership for supporting high-quality and high-impact

research in information and communications technology. To support this

mission, the universities host the Helsinki Institute for Information

Technology (HIIT), and the Finnish Center for Artificial Intelligence

(FCAI), which is one of the 10 national flagships nominated by the

Academy of Finland. The mission of the Helsinki Institute for

Information Technology HIIT is to enhance the quality, visibility and

impact of Finnish research on information technology and support

cooperation between ICT researchers, researchers in other fields,

industry and public organisations.

We are seeking to fulfil

Postdoctoral positions for 39 projects at our hosting universities. In

addition, we are also looking to support up to 10 outstanding

researchers in their career development through our HIIT Postdoctoral Fellowship and HIIT Research Fellowship Programmes prioritising the following HIIT focus areas:

• Artificial Intelligence
• Computational Health
• Cybersecurity
• Data Science
• Foundations of Computing

We

are committed to fostering an inclusive environment with people from

diverse backgrounds, and researchers from underrepresented groups are

particularly encouraged to apply.

The deadline for applications is January 30th, 2022 at 11:59pm Finnish Time (Eastern European Standard Time) (GMT + 2)

The

following projects are included in this open call. Projects H1-H21 are

funded by Researchers or Research Groups. Projects F0-F16 are a part of

FCAI.

For more detailed descriptions of these projects, please visit our website

H1: Machine learning in precision oncology

H2: Machine Learning for Health (ML4H)

H3:Statistical Genetics and Machine Learning

H4:Bayesian workflows for iterative model building and networks of models

H5:Bayesian Inference and Machine Learning

H6:Deep Generative Modeling for Precision Medicine and Future Clinical Trials

H7:Bayesian deep learning for continuous-time dynamical systems

H8:Deep Representation Learning – Foundations and New Directions

H9:Machine learning for Human-AI collaboration

H10:Foundations of Computing

H11:Human Sciences – Computing Interaction (HSCI)

H12: AI technologies for interaction prediction in biomedicine

H13: FoTran - Found in translation

H14: Lifecycle support of machine learning applications

H15: Aalto Human-Computer Interaction and Security Engineering Lab

H16: Eco-Evolutionary Control Theory

H17: Computational Biology

H18: AI algorithms for quantitative biology

H19:Variable selection with missing data with applications to genetics

H20: Reconstructing Crisis Narratives for Trustworthy Communication and Cooperative Agency

H21:Sustainable ICT

F0: Virtual Laboratories assisted by collaborative AI: From Foundations to Practice

F1: Virtual Laboratories: Multi-level Simulation for Sustainable Autonomy

F2:Virtual Laboratories: Closing Simulation - Real World Gap

F3: Virtual Atmospheric Laboratory

F4: Virtual Laboratories: Synthetic Psychologist

F5: Virtual laboratories: Drug Design

F6: AI-assisted design

F7: AI-assisted modeling

F8: Collaborative AI for AI-assisted decision making

F9: Machine learning for collaborative AI

F10: ELFI: Engine for Likelihood-free Inference

F11: Design of Maximally Autonomous Collaborative AI Systems

F12: Societal aspects of AI

F13: Machine learning to integrate family structure into health trajectories across 1.7 million individuals

F14: Computational Rationality

F15: Privacy-preserving and federated learning

F16: Reinforcement learning under uncertainty