Simula Research Laboratory AS

Fellowship ID:

Simula-POSTDOC1 [#28035]

Fellowship Title:

Postdoctoral Research Fellowship in foundations and algorithm design for Machine Learning, SURE-AI

Fellowship Type:

Postdoctoral

Fellowship Location:

Oslo, Oslo 0164, Norway

Appl Deadline:

2026/04/01 11:59PM help popup

(posted 2026/01/28, listed until 2026/04/01)

Fellowship Description:

A Postdoctoral Research Fellowship is available at Simula Metropolitan Center for Digital Engineering (SimulaMet), within the Department of Signal and Information Processing for Intelligent Systems (SIGIPRO).

This position is part of the National Norwegian Centre for Sustainable, Risk-averse and Ethical AI (SURE-AI), funded by the Research Council of Norway (RCN) (2025-2030), coordinated by Simula Research Laboratory (Simula). Simula is a leading Norwegian research institute known for its excellence in cutting-edge ICT with a strong track record of top evaluations, active international collaborations, and successful in significant funding initiatives, including European and National RCN grants. SimulaMet is a research centre jointly owned by Simula and Oslo Metropolitan University. It is the home of Simula’s research activities on networks and communications, artificial intelligence and IT management, and it is OsloMet’s strategic partner in digital engineering. SimulaMet supports talent development through PhD and postdoctoral programs and provides valuable research infrastructure, such as the national HPC facility, eX3, and network testbeds.

Job Description

The postdoctoral researcher position will be part of an interdisciplinary research environment including international collaborators. The Postdoctoral researcher is expected to cover the three fronts of theoretical analysis (e.g. performance guarantees), algorithm design and implementation, carrying out research on the following topics: a) learning explainable representations capturing optimally the complex spatio-temporal dependencies and geometry in multivariate data, as well as exploiting those learned representations for robust inference, reasoning, decision-making and control tasks, while allowing to incorporate knowledge and tracking uncertainties, b) constrained machine learning algorithms that embed safety, ethics, and regulatory requirements directly into training and decision-making processes, such as aligning Foundation Models with human values and designing risk-aware reinforcement learning frameworks, c) continual and online learning methods that adapt to time-varying distributions and non-IID data while providing formal generalization guarantees. By leveraging structural dependence representation learning, counterfactual reasoning and automated hyperparameter optimization, it will provide robust out-of-distribution performance and stability across non-stationary environments.

All these projects/topics will incorporate real data in the context of impactful applications within the verticals of energy, finance and societal systems, in cooperation with some of the public or private partners involved and postdoctoral/senior researchers in the SURE-AI Centre.

Research Statement: Applicants are required to prepare a (max) 2-page research statement providing a personal view about the main research questions they are interested in, their relevance to this position, as well as about the key theoretical and methodological approaches to be followed. Applicants are encouraged to propose research ideas that combine rigorous ML designs, underlying mathematical methods, with exploratory, or unconventional problem formulations.

Selection criteria

We will consider candidates who have a PhD in Electrical Engineering, Computer Science, Applied Mathematics or other related relevant discipline. The applicant is required to demonstrate how the degree corresponds to the profile of the post. Other required qualifications include:

A strong background in optimization, data science, machine learning and signal processing, proven by a high-quality publication record.
Previous experience in both designing novel data-driven learning and control methods and applying them to real data, as proved by publicly released code and publications.
Excellent software development skills demonstrated by previously released software packages, fluency in Python and/or Matlab.
Excellent oral and written communication skills in English and a record of previous talks at international conferences.
Ambition to carry out interdisciplinary research and willingness to work as part of an international team.
Interest in and ability to collaborate across disciplines and institutions.
Relevance of profile to SURE-AI’s aims and objectives.
Sharing Simula’s core values: excellence, impact, curiosity, ambition and respect.

For all application details and more information about the position, Simula and the SURE-AI Centre, please read the full announcement.

We are not accepting applications for this job through MathJobs.Org right now. Please apply at https://www.simula.no/careers/job-openings/postdoctoral-research-fellowship-in-foundations-and-algorithm-design-for-machine-learning-sure-ai .

Contact: Baltasar Beferull-Lozano

Email: email address

Postal Mail:

: Kristian Augusts gate 23
0164 Oslo
Norway

Web Page: simula.no